Differential analysis
Read in all the DPE files calculated by JF Trempe Lab or/and TMT-analyst by RL Files separated by genotype/iPSC line in the workbook “ProcessFilesRenameAccession.Rmd” All data is from 6 weeks DANs from iPSC lines in AIW002-02 background Bright genome and dark genome where run separately
# read in csv into to make a list of dataframes
# Load required library
library(readr)
# the protein DE files are here
folder_path <- "/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/DPE_files/"
# List all CSV files in the folder
csv_files <- list.files(path = folder_path, pattern = "\\.csv$", full.names = TRUE)
# Read all CSV files into a list of dataframes, skipping the first column
df_list <- lapply(csv_files, function(file) {
read_csv(file, col_types = cols(.default = "?", `...1` = col_skip()))
})
New names:
• `` -> `...1`
New names:
• `` -> `...1`
New names:
• `` -> `...1`
New names:
• `` -> `...1`
New names:
• `` -> `...1`
New names:
• `` -> `...1`
New names:
• `` -> `...1`
New names:
• `` -> `...1`
# Optionally, name each element of the list with the respective file names (without the .csv extension)
names(df_list) <- tools::file_path_sans_ext(basename(csv_files))
# Print the names of the dataframes
print(names(df_list))
[1] "GBA-KO_ProtomicsDifferentialAbundance" "IGSF9B-KO_ProtomicsDifferentialAbundance" "INPP5F-KO_ProtomicsDifferentialAbundance"
[4] "IP6K2-KO_ProtomicsDifferentialAbundance" "PINK1-KO_ProtomicsDifferentialAbundance" "PRKN-KO_ProtomicsDifferentialAbundance"
[7] "SH3GL2-KO_ProtomicsDifferentialAbundance" "SNCA-A53T_ProtomicsDifferentialAbundance"
# test that these are dataframes
df.gba <- df_list$`GBA-KO_ProtomicsDifferentialAbundance`
head(df.gba)
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NA
Rename the list
print(names(df_list))
[1] "GBA-KO" "IGSF9B-KO" "INPP5F-KO" "IP6K2-KO" "PINK1-KO" "PRKN-KO" "SH3GL2-KO" "SNCA-A53T"
head(df_list$`GBA-KO`)
Thresholds: Log2 abundance ratio > 0.5 and p value < 0.05
pSH3GL2 <- EnhancedVolcano(df_list$`SH3GL2-KO`,
lab = df_list$`SH3GL2-KO`$Symbol,
x = 'log2_ratio',
y = 'p-value',
pCutoff = 0.05,
FCcutoff = 0.5,
colAlpha = 0.5,
labSize = 5,
xlim = c(-3,2.5),
ylim = c(0, 10.5),
drawConnectors = FALSE,
widthConnectors = 0.1,
max.overlaps = 40,
legendPosition = "right",
title = "SH3GL2-KO vs Control",
subtitle = "Differential Protein Abundance"
) + scale_x_continuous(breaks = seq(-3, 2.5, by = 0.5)) + # Adjust the x-axis breaks as needed
scale_y_continuous(limits = c(0, 10.5), expand = c(0, 0)) + # Remove space below the y-axis
coord_cartesian(xlim = c(-3, 2.5), ylim = c(0, 10.5)) + # Ensure that the plot does not display points beyond this range
theme(axis.text.x = element_text(size = 14), axis.text.y = element_text(size = 14)) # Adjust x-axis label size
Scale for x is already present.
Adding another scale for x, which will replace the existing scale.
Scale for y is already present.
Adding another scale for y, which will replace the existing scale.
Coordinate system already present. Adding new coordinate system, which will replace the existing one.
pA53T
pGBA
pPINK1
pPRKN
pIGSF9B
pINPP5F
pIP6K2
pSH3GL2
Make a filtered list of dataframes with the thresholds
colnames(df_list$`GBA-KO`)
[1] "Accession" "Symbol" "Description" "log2_ratio" "p-value"
head(df_list$`IGSF9B-KO`)
head(df_list_numeric$`IGSF9B-KO`)
filter_dge_lists <- function(dge_lists, logFC_threshold = 0.25, logFC_direction = "both", p_threshold = 0.01, p_col = "p-value") {
# Iterate over each dataframe in the list
dge_lists_filtered <- lapply(dge_lists, function(dge_df) {
# Debugging: Print column names of the dataframe being processed
print(paste("Processing dataframe with columns:", paste(colnames(dge_df), collapse = ", ")))
# Apply the filter function to each dataframe
filtered_df <- filter_dge_results(dge_df, logFC_threshold, logFC_direction, p_threshold, p_col)
return(filtered_df)
})
return(dge_lists_filtered)
}
# Example usage
filtered_DEP <- filter_dge_lists(df_list, logFC_threshold = 0.5, logFC_direction = "both", p_threshold = 0.05, p_col = "p-value")
[1] "Processing dataframe with columns: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Columns in dataframe: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Classes of columns: character, character, character, numeric, numeric"
[1] "Processing dataframe with columns: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Columns in dataframe: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Classes of columns: character, character, character, numeric, numeric"
[1] "Processing dataframe with columns: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Columns in dataframe: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Classes of columns: character, character, character, numeric, numeric"
[1] "Processing dataframe with columns: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Columns in dataframe: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Classes of columns: character, character, character, numeric, numeric"
[1] "Processing dataframe with columns: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Columns in dataframe: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Classes of columns: character, character, character, numeric, numeric"
[1] "Processing dataframe with columns: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Columns in dataframe: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Classes of columns: character, character, character, numeric, numeric"
[1] "Processing dataframe with columns: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Columns in dataframe: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Classes of columns: character, character, character, numeric, numeric"
[1] "Processing dataframe with columns: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Columns in dataframe: Accession, Symbol, Description, log2_ratio, p-value"
[1] "Classes of columns: character, character, character, numeric, numeric"
Get gene counts
# function to count up and down regulated Proteins
count_regulations <- function(dge_df) {
# Ensure columns are numeric
dge_df$log2_ratio <- as.numeric(dge_df$log2_ratio)
# Count upregulated and downregulated proteins
upregulated_count <- sum(dge_df$log2_ratio > 0, na.rm = TRUE)
downregulated_count <- sum(dge_df$log2_ratio < 0, na.rm = TRUE)
return(c(Upregulated = upregulated_count, Downregulated = downregulated_count))
}
summarize_regulations <- function(dge_lists) {
# Get names of the dataframes
df_names <- names(dge_lists)
# Apply the counting function to each dataframe and name the result
counts_list <- lapply(dge_lists, function(df) {
counts <- count_regulations(df)
return(counts)
})
# Convert the list of counts into a dataframe
result_df <- do.call(rbind, counts_list)
# Set the row names to the names of the original dataframes
rownames(result_df) <- df_names
return(result_df)
}
# apply to filtered list
regulation_summary <- summarize_regulations(filtered_DEP)
print(regulation_summary)
Upregulated Downregulated
GBA-KO 109 137
IGSF9B-KO 559 738
INPP5F-KO 144 209
IP6K2-KO 59 87
PINK1-KO 274 496
PRKN-KO 373 573
SH3GL2-KO 133 116
SNCA-A53T 162 244
colnames(filtered_DEP$`PINK1-KO`)
[1] "Accession" "Symbol" "Description" "log2_ratio" "p-value"
Function to get the top n genes up and down
# Function to select top n up and down regulated genes
select_top_genes <- function(dge_df, logFC_col = "log2_ratio", symbol_col = "Symbol", n = 10) {
# Ensure log2_ratio column is numeric
dge_df[[logFC_col]] <- as.numeric(dge_df[[logFC_col]])
# Sort dataframe by log2_ratio to get top upregulated and downregulated genes
top_upregulated <- dge_df %>%
arrange(desc(!!sym(logFC_col))) %>%
head(n) %>%
pull(!!sym(symbol_col))
top_downregulated <- dge_df %>%
arrange(!!sym(logFC_col)) %>%
head(n) %>%
pull(!!sym(symbol_col))
# Combine upregulated and downregulated genes into a single vector
top_genes <- c(top_upregulated, top_downregulated)
return(top_genes)
}
# Example usage
top_genes <- select_top_genes(filtered_DEP$`PINK1-KO`, n = 10)
print(top_genes)
[1] "NEFL" "PLXNA4" "VSNL1" "NEFM" "PALM3" "STX1A" "ATP5PF" "SLC25A13" "SYT2" "DIRAS2" "CA2"
[12] "SNRPA" "PROCR" "HDGF" "A0A087WY61" "ZNF207" "CD99" "HMGN3" "DCN" "LMNA"
top_genes <- select_top_genes(filtered_DEP$`PINK1-KO`, n = 5)
print(top_genes)
[1] "NEFL" "PLXNA4" "VSNL1" "NEFM" "PALM3" "CA2" "SNRPA" "PROCR" "HDGF" "A0A087WY61"
df.pink1 <- filtered_DEP$`PINK1-KO`
Plot a heatmap of the top up and down genes
heatmap_plot <- plot_protein_heatmap(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO")
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"
# Print the plot
print(heatmap_plot)
Plot control and IPSC line for each list
# Example usage
# Assuming 'df' is your dataframe with relative abundance data
heatmap_plot <- plot_protein_heatmap(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO")
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"
# Print the plot
print(heatmap_plot)
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z-score
#library(ggplot2)
#library(dplyr)
#library(tidyr)
#data = df
#proteins = top_genes
#sample_patterns = c("Control", "PINK1.KO")
# Function to plot heatmap of relative abundance with Z-scores
plot_protein_heatmap_zscore <- function(data, proteins, sample_patterns, na_color = "grey"){
# Filter the data for selected proteins
data_filtered <- data %>%
filter(Symbol %in% proteins)
# Set the order of the Symbol factor based on the input vector 'proteins'
data_filtered$Symbol <- factor(data_filtered$Symbol, levels = proteins)
# Identify sample columns matching patterns
sample_columns <- colnames(data_filtered)[sapply(colnames(data_filtered), function(col_name) {
any(sapply(sample_patterns, function(p) grepl(p, col_name)))
})]
# Debug: Check contents of sample_columns
print(paste("Sample columns selected:", paste(sample_columns, collapse = ", ")))
# Check if sample_columns has valid entries
if (length(sample_columns) == 0) {
stop("No sample columns matched the patterns provided.")
}
# Select only columns matching sample patterns and the Symbol column
data_filtered <- data_filtered %>%
dplyr::select(Symbol, all_of(sample_columns))
# Remove rows where all values are NA (excluding the Symbol column)
data_filtered <- data_filtered %>%
filter(rowSums(is.na(dplyr::select(., -Symbol))) < length(sample_columns))
# Calculate Z-scores for each protein across the selected samples
data_zscore <- data_filtered %>%
mutate(across(all_of(sample_columns), ~ scale(.)[, 1], .names = "z_{col}"))
# Reshape data for ggplot
data_long <- data_zscore %>%
pivot_longer(
cols = starts_with("z_"),
names_to = "Sample",
values_to = "Abundance"
) %>%
mutate(Sample = gsub("z_", "", Sample)) # Remove 'z_' prefix for clean sample names
# Create the heatmap
heatmap_plot <- ggplot(data_long, aes(x = Sample, y = Symbol, fill = Abundance)) +
geom_tile(color = "white") +
scale_fill_gradientn(
#colors = c("#ffffff", "#ffcccc", "#ff6666", "#ff3333","#fa0505", "#cc0000","#990000"),
#colors = c("#fdfef4", "#DAF7A6", "#FFC300", "#FF5733","#e71f05","#4d0b02"),
colors = c("snow","lightgoldenrod1","gold1","darkorange1","red2","firebrick4"),
values = scales::rescale(c(-0.5, -0.25, 0, 1,2,2.5,2.75)),
na.value = na_color,
guide = guide_colorbar(
barwidth = 1,
barheight = 10,
title.position = "top",
title.hjust = 0.5
)) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Protein Abundance Heatmap (Z-Score)", x = "Samples", y = "Proteins")
return(heatmap_plot)
}
# Example usage
heatmap_plot <- plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO")
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"
# Print the plot
print(heatmap_plot)
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NA
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Adjust the function
library(ggplot2)
library(dplyr)
library(tidyr)
# Function to plot heatmap of relative abundance with Z-scores
plot_protein_heatmap_zscore <- function(data, proteins, sample_patterns, colors, scale_values, na_color = "grey") {
# Filter the data for selected proteins
data_filtered <- data %>%
filter(Symbol %in% proteins)
# Set the order of the Symbol factor based on the input vector 'proteins'
data_filtered$Symbol <- factor(data_filtered$Symbol, levels = proteins)
# Identify sample columns matching patterns
sample_columns <- colnames(data_filtered)[sapply(colnames(data_filtered), function(col_name) {
any(sapply(sample_patterns, function(p) grepl(p, col_name)))
})]
# Debug: Check contents of sample_columns
print(paste("Sample columns selected:", paste(sample_columns, collapse = ", ")))
# Check if sample_columns has valid entries
if (length(sample_columns) == 0) {
stop("No sample columns matched the patterns provided.")
}
# Select only columns matching sample patterns and the Symbol column
data_filtered <- data_filtered %>%
dplyr::select(Symbol, all_of(sample_columns))
# Remove rows where all values are NA (excluding the Symbol column)
data_filtered <- data_filtered %>%
filter(rowSums(is.na(dplyr::select(., -Symbol))) < length(sample_columns))
# Calculate Z-scores for each protein across the selected samples
data_zscore <- data_filtered %>%
mutate(across(all_of(sample_columns), ~ scale(.)[, 1], .names = "z_{col}"))
# Reshape data for ggplot
data_long <- data_zscore %>%
pivot_longer(
cols = starts_with("z_"),
names_to = "Sample",
values_to = "Abundance"
) %>%
mutate(Sample = gsub("z_", "", Sample)) # Remove 'z_' prefix for clean sample names
# Create the heatmap
heatmap_plot <- ggplot(data_long, aes(x = Sample, y = Symbol, fill = Abundance)) +
geom_tile(color = "white") +
scale_fill_gradientn(
colors = colors,
values = scales::rescale(scale_values),
na.value = na_color,
guide = guide_colorbar(
barwidth = 1,
barheight = 10,
title.position = "top",
title.hjust = 0.5
)
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Protein Abundance Heatmap (Z-Score)", x = "Samples", y = "Proteins")
return(heatmap_plot)
}
# Example usage
heatmap_plot <- plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-2.5, -2, -1, 0, 1, 2, 2.5) # Adjust based on your data range
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"
# Print the plot
print(heatmap_plot)
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NA
# Example usage
heatmap_plot <- plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-2.5, -2, -1, 0, 1, 2, 2.5), # Adjust based on your data range
group_means = TRUE # Set to FALSE if you want individual samples
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"
# Print the plot
print(heatmap_plot)
# Example usage
heatmap_plot <- plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-2.5, -2, -1, 0, 1, 2, 2.5), # Adjust based on your data range
group_means = FALSE # Set to FALSE if you want individual samples
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"
# Print the plot
print(heatmap_plot)
Control width
library(ggplot2)
library(dplyr)
library(tidyr)
# Function to plot heatmap of relative abundance with Z-scores
plot_protein_heatmap_zscore <- function(data, proteins, sample_patterns, colors, scale_values, na_color = "grey", group_means = FALSE, tile_width = 0.9) {
# Filter the data for selected proteins
data_filtered <- data %>%
filter(Symbol %in% proteins)
# Set the order of the Symbol factor based on the input vector 'proteins'
data_filtered$Symbol <- factor(data_filtered$Symbol, levels = proteins)
# Identify sample columns matching patterns
sample_columns <- colnames(data_filtered)[sapply(colnames(data_filtered), function(col_name) {
any(sapply(sample_patterns, function(p) grepl(p, col_name)))
})]
# Debug: Check contents of sample_columns
print(paste("Sample columns selected:", paste(sample_columns, collapse = ", ")))
# Check if sample_columns has valid entries
if (length(sample_columns) == 0) {
stop("No sample columns matched the patterns provided.")
}
# Select only columns matching sample patterns and the Symbol column
data_filtered <- data_filtered %>%
dplyr::select(Symbol, all_of(sample_columns))
# Remove rows where all values are NA (excluding the Symbol column)
data_filtered <- data_filtered %>%
filter(rowSums(is.na(dplyr::select(., -Symbol))) < length(sample_columns))
if (group_means) {
# Group samples by the base name and calculate mean
sample_base <- gsub("\\.\\d+$", "", colnames(data_filtered)[-1])
data_grouped <- data_filtered %>%
pivot_longer(cols = -Symbol, names_to = "Sample", values_to = "Abundance") %>%
mutate(SampleBase = gsub("\\.\\d+$", "", Sample)) %>%
group_by(Symbol, SampleBase) %>%
summarize(Abundance = mean(Abundance, na.rm = TRUE), .groups = 'drop') %>%
pivot_wider(names_from = SampleBase, values_from = Abundance)
# Calculate Z-scores
data_zscore <- data_grouped %>%
mutate(across(-Symbol, ~ scale(.)[, 1], .names = "z_{col}"))
# Reshape data for ggplot
data_long <- data_zscore %>%
pivot_longer(
cols = starts_with("z_"),
names_to = "Sample",
values_to = "Abundance"
) %>%
mutate(Sample = gsub("z_", "", Sample)) # Remove 'z_' prefix for clean sample names
} else {
# Calculate Z-scores for each protein across the selected samples
data_zscore <- data_filtered %>%
mutate(across(all_of(sample_columns), ~ scale(.)[, 1], .names = "z_{col}"))
# Reshape data for ggplot
data_long <- data_zscore %>%
pivot_longer(
cols = starts_with("z_"),
names_to = "Sample",
values_to = "Abundance"
) %>%
mutate(Sample = gsub("z_", "", Sample)) # Remove 'z_' prefix for clean sample names
}
# Create the heatmap
heatmap_plot <- ggplot(data_long, aes(x = Sample, y = Symbol, fill = Abundance)) +
geom_tile(color = "white") +
scale_fill_gradientn(
colors = colors,
values = scales::rescale(scale_values),
na.value = na_color,
guide = guide_colorbar(
barwidth = 1,
barheight = 10,
title.position = "top",
title.hjust = 0.5
)
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
aspect.ratio = 1 / tile_width # Adjust aspect ratio
) +
labs(title = "Protein Abundance Heatmap (Z-Score)", x = "Samples", y = "Proteins")
return(heatmap_plot)
}
# Example usage
heatmap_plot <- plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-2.5, -2, -1, 0, 1, 2, 2.5), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25 # Adjust the width of the tiles (default is 0.9)
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"
# Print the plot
print(heatmap_plot)
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NA
Function to see the gene expression
max(df.long$Abundance)
[1] 3.575772
min(df.long$Abundance)
[1] -0.490151
Check each contrast
top_genes <- select_top_genes(filtered_DEP$`PINK1-KO`, n = 10)
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-0.5, -0.25, 0, 1, 2, 4), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-0.5, -0.25, 0, 1, 2, 4), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-0.9,-0.6,-0.2, 0, 2, 2.5), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PINK1.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-0.9,-0.6,-0.2, 0, 2, 2.5), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3"
colnames(df)
[1] "Symbol" "A53T.1" "A53T.2" "A53T.3" "Control.1" "Control.2" "Control.3" "GBA.KO.1" "GBA.KO.2" "GBA.KO.3" "PINK1.KO.1"
[12] "PINK1.KO.2" "PINK1.KO.3" "PRKN.KO.1" "PRKN.KO.2" "PRKN.KO.3"
SNCA-A53T
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "A53T"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5,-0.25, 0, 1, 2, 4), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: A53T.1, A53T.2, A53T.3, Control.1, Control.2, Control.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "A53T"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5, -0.25,0, 1, 2, 4), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: A53T.1, A53T.2, A53T.3, Control.1, Control.2, Control.3"
GBA
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "GBA.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, GBA.KO.1, GBA.KO.2, GBA.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "GBA.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, GBA.KO.1, GBA.KO.2, GBA.KO.3"
Parkin KO
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PRKN.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PRKN.KO.1, PRKN.KO.2, PRKN.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "PRKN.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, PRKN.KO.1, PRKN.KO.2, PRKN.KO.3"
For the dark genome gene expression levels I’ll need the other dataframe
colnames(df)
[1] "Symbol" "Control.1" "Control.2" "Control.3" "Control.4" "IGSF9B.KO.1" "IGSF9B.KO.2" "INPP5F.KO.1" "INPP5F.KO.2" "INPP5F.KO.3"
[11] "IP6K2.KO.1" "IP6K2.KO.2" "IP6K2.KO.4" "SH3GL2.KO.1" "SH3GL2.KO.2" "SH3GL2.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "IGSF9B.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "IGSF9B.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5, -0.25,0, 1, 2, 3.5), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "INPP5F.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
scale_values = c(-0.75, -0.5, -0.25,0, 1, 2, 4), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, INPP5F.KO.1, INPP5F.KO.2, INPP5F.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "INPP5F.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
scale_values = c(-0.75, -0.5, -0.25,0, 1, 2, 4), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, INPP5F.KO.1, INPP5F.KO.2, INPP5F.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "IP6K2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
scale_values = c(-0.75, -0.5, -0.25,0, 1, 2, 4), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IP6K2.KO.1, IP6K2.KO.2, IP6K2.KO.4"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "IP6K2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
scale_values = c(-0.75, -0.5, -0.25,0, 1, 2, 4), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IP6K2.KO.1, IP6K2.KO.2, IP6K2.KO.4"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "SH3GL2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
scale_values = c(-0.6, -0.5,0.25, 0, 1, 3.8, 4.1), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
plot_protein_heatmap_zscore(
data = df,
proteins = top_genes, # Example protein names
sample_patterns = c("Control", "SH3GL2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
scale_values = c(-0.6, -0.5,0.25, 0, 1, 3.8, 4.1), # Adjust based on your data range
group_means = FALSE,# Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
names(filtered_DEP)
[1] "GBA-KO" "IGSF9B-KO" "INPP5F-KO" "IP6K2-KO" "PINK1-KO" "PRKN-KO" "SH3GL2-KO" "SNCA-A53T"
# Create the UpSet plot
upset(
gene_matrix,
sets = names(gene_lists),
sets.bar.color = "#56B4E9",
order.by = "freq",
empty.intersections = "on",
keep.order = TRUE
)
Control the order
p <- create_upset_plot(filtered_DEP, contrast_order, colors = rev(colors), text_scale = 1.5)
'data.frame': 2314 obs. of 9 variables:
$ Symbol : chr "HDLBP" "EHD1" "OSTC" "VTA1" ...
$ IP6K2-KO : num 0 0 0 0 0 0 0 0 0 0 ...
$ SH3GL2-KO: num 0 0 0 0 0 0 0 0 0 0 ...
$ INPP5F-KO: num 0 0 0 0 0 0 0 0 0 0 ...
$ IGSF9B-KO: num 0 0 1 1 1 0 1 0 0 1 ...
$ PRKN-KO : num 1 1 0 1 0 0 1 0 1 1 ...
$ PINK1-KO : num 1 1 1 1 0 0 1 0 1 1 ...
$ GBA-KO : num 1 1 1 1 1 1 1 1 1 1 ...
$ SNCA-A53T: num 1 0 1 0 0 0 1 1 1 1 ...
NULL
print(p)
pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/UpsetplotBrightandDark.pdf", width = 10, height = 5.5)
p
dev.off()
null device
1
See which genes overlap - function
# bright genome overlap
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO","PRKN-KO") # Specify the contrasts of interest
result.bright <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)
# Print results
print(result.bright$`Overlapping Genes`) # Genes common across all specified contrasts
[1] "HDLBP" "NCAM1" "A0A0J9YYL3" "STXBP1" "A0A1P0AYU5" "RPS14" "PALM3" "HSPA12A" "PRKRA" "P4HB" "OBSCN"
[12] "SNRPE" "SCFD1" "CYCS" "CTSD" "RPS3A" "SNCA" "NEFM" "TP53I11" "F8W6I7" "CD44" "H3BQZ7"
[23] "CBX1" "SLC16A3" "HNRNPUL1" "C11orf58" "VGF" "DCX" "KPNA6" "CLN5" "SNCG" "LY6H" "HRAS"
[34] "FUCA1" "ANXA1" "ATP1B1" "SSB" "KRT18" "PYGL" "NEFL" "ANXA2" "DCN" "SYP" "ANXA5"
[45] "GSTP1" "GNAO1" "HSPA5" "RALA" "ATP1A3" "P4HA1" "GNS" "PPP3CB" "SYN1" "GAP43" "NCL"
[56] "FLNA" "MAOA" "HNRNPA2B1" "ATP2B4" "FKBP2" "EPHB2" "EEF1D" "CRABP1" "PRDX6" "LRPAP1" "RPS19"
[67] "PRPH" "ACAA2" "SRP9" "SERPINH1" "HDGF" "RPS8" "RPS23" "RPS13" "RPL23A" "GNB1" "RPL10A"
[78] "ABAT" "ERH" "SET" "PLOD1" "NUCB1" "CRYZ" "LGALS3BP" "AHNAK" "GALNT2" "CNTN1" "DNAJC3"
[89] "TRIM28" "ALCAM" "STX1A" "FNDC3B" "GDAP1L1" "CEP68" "CEND1" "CADM2" "GLG1" "CMBL" "DIRAS2"
[100] "FUCA2" "GOLPH3" "CTPS2" "TMOD3" "NTM" "RRBP1" "PLXNA1" "SEC23IP"
#print(result$`Unique Genes`) # List of genes unique to each contrast
Function isn’t exactly correct need to fix
intersect(pink.df$Accession, prkn.df$Accession)
[1] "A0A024R4E5" "A0A024R571" "A0A087WTT1" "A0A087WWD4" "A0A087WWU8" "A0A087WY55" "A0A0A0MQX8" "A0A0A6YYA0" "A0A0D9SF30" "A0A0D9SF98" "A0A0J9YXF2"
[12] "A0A0J9YYL3" "A0A1B0GTP9" "A0A1P0AYU5" "A0A1W2PQR6" "A0A2R8Y5S7" "A0A2R8Y6W5" "A0A2R8Y811" "A0A2R8Y849" "A0A2R8YDT1" "A0A3B3IU69" "A0A494BWY4"
[23] "A0A494C1E2" "A0A590UJ23" "A0A5F9UP49" "A0A5F9ZHL1" "A0A669KB89" "A0A6I8PIW1" "A0A6Q8PFE5" "A0A6Q8PFJ0" "A0A6Q8PGS2" "A0A7I2V3I2" "A0A7I2V4B3"
[34] "A0A7I2V4E4" "A0A7I2V535" "A0A7I2V5H3" "A0A7I2YQT2" "A0A7P0PJI2" "A0A7P0TA35" "A0A7P0TA76" "A0A7P0TAG7" "A0A7P0TAQ9" "A0A804HK65" "A0A804HL40"
[45] "A2A2V1" "A6NFX8" "A6NGQ3" "A6NHK2" "A6NNK5" "A8MU27" "A8MXP9" "B0QZK4" "B1ALY0" "B3KS98" "B4DHE8"
[56] "B4DLN1" "B4DLR8" "B5ME19" "B7Z5N7" "B7ZC39" "C9J3D7" "C9J813" "C9JFR7" "C9JH19" "C9JZR2" "D6RAT0"
[67] "D6REX3" "D6RH20" "E2QRB3" "E7EPV7" "E7ESP9" "E7EUC7" "E7EX17" "E7EX73" "E9PC15" "E9PDF2" "E9PF59"
[78] "E9PFH4" "E9PHY5" "E9PIN5" "E9PJP2" "E9PNF7" "F5GWR7" "F5GYQ1" "F5GZS6" "F8VW96" "F8VZX2" "F8W6I7"
[89] "G3V0I5" "G3V186" "G3V1L9" "G3V3E8" "H0Y3P2" "H0Y938" "H0YD13" "H0YHX9" "H0YKD8" "H3BN98" "H3BQZ7"
[100] "H3BQZ9" "H3BUF6" "H7BZJ3" "H7C1N3" "H7C2N1" "H9KV31" "J3KNP2" "J3KS05" "J3KS31" "J3QQV2" "K7ELL7"
[111] "K7ENE8" "K7ERC8" "M0R0F0" "M0R181" "M0R210" "M0R3F1" "O00193" "O00425" "O00461" "O00625" "O00754"
[122] "O15240" "O43143" "O43175" "O43181" "O43242" "O43399" "O43602" "O43615" "O43639" "O43684" "O60313"
[133] "O60684" "O60763" "O60831" "O60841" "O75306" "O75368" "O75369" "O75390" "O75475" "O75489" "O75503"
[144] "O75525" "O75718" "O75746" "O76003" "O76070" "O94772" "O94826" "O94925" "O95197" "O95302" "O95336"
[155] "O95631" "P00390" "P00403" "P00505" "P00738" "P01112" "P02786" "P04066" "P04080" "P04083" "P04181"
[166] "P04843" "P05026" "P05204" "P05413" "P05455" "P05783" "P05787" "P05937" "P06737" "P06756" "P07196"
[177] "P07355" "P07585" "P07858" "P07954" "P08247" "P08670" "P08758" "P08865" "P09211" "P09471" "P09874"
[188] "P09960" "P09972" "P10155" "P10253" "P10909" "P11021" "P11233" "P11279" "P11413" "P12004" "P12036"
[199] "P12532" "P13010" "P13637" "P13667" "P13674" "P14406" "P15289" "P15586" "P15880" "P16152" "P16278"
[210] "P16298" "P16615" "P16671" "P17050" "P17600" "P17677" "P18077" "P18859" "P19338" "P19367" "P20020"
[221] "P20073" "P20962" "P21283" "P21333" "P21397" "P21796" "P21912" "P22307" "P22626" "P23396" "P23526"
[232] "P23634" "P24539" "P25398" "P25705" "P25788" "P26012" "P26232" "P26373" "P26641" "P26885" "P26992"
[243] "P27635" "P27797" "P29323" "P29401" "P29692" "P29762" "P30041" "P30044" "P30533" "P30536" "P30837"
[254] "P31689" "P32004" "P32189" "P32969" "P35221" "P35240" "P36542" "P37108" "P37837" "P38435" "P39019"
[265] "P39023" "P40261" "P40926" "P41219" "P42765" "P43121" "P43155" "P45973" "P46783" "P46940" "P46977"
[276] "P48444" "P48681" "P49189" "P49458" "P49755" "P49915" "P50148" "P50454" "P50995" "P51571" "P51572"
[287] "P51798" "P51858" "P52306" "P52655" "P52788" "P52815" "P53396" "P53618" "P53621" "P54709" "P54727"
[298] "P54802" "P55036" "P55809" "P58876" "P59768" "P60033" "P60174" "P60842" "P60866" "P60880" "P61224"
[309] "P61764" "P62081" "P62241" "P62266" "P62269" "P62277" "P62701" "P62750" "P62760" "P62873" "P62888"
[320] "P62906" "P63096" "P63220" "P63244" "P67936" "P68104" "P78357" "P78527" "P80303" "P80404" "P80723"
[331] "P83731" "P84090" "Q00577" "Q00688" "Q01105" "Q01581" "Q02809" "Q02818" "Q06323" "Q08209" "Q08211"
[342] "Q08257" "Q08380" "Q09666" "Q10471" "Q12841" "Q12860" "Q12907" "Q13217" "Q13263" "Q13310" "Q13442"
[353] "Q13740" "Q14152" "Q14315" "Q15019" "Q15102" "Q15149" "Q15365" "Q15369" "Q15424" "Q15555" "Q15651"
[364] "Q15768" "Q16186" "Q16352" "Q16531" "Q16623" "Q16778" "Q32P28" "Q4J6C6" "Q53EP0" "Q5JXI8" "Q5SQI0"
[375] "Q5SW79" "Q5SWX8" "Q5T1M5" "Q5T760" "Q5T7C4" "Q5TE61" "Q5URX0" "Q5ZPR3" "Q60FE5" "Q6DKJ4" "Q6P587"
[386] "Q6X4W1" "Q76N32" "Q7KZF4" "Q7L0Y3" "Q7Z4G1" "Q86TU7" "Q86UY8" "Q8N111" "Q8N3J6" "Q8N8L6" "Q8NBU5"
[397] "Q8NC51" "Q8TAT6" "Q8TB37" "Q8TCJ2" "Q8WVV9" "Q8WXD2" "Q92743" "Q92859" "Q92879" "Q92896" "Q92945"
[408] "Q969H8" "Q969X5" "Q96AQ6" "Q96AX1" "Q96AY3" "Q96CW9" "Q96DA6" "Q96DG6" "Q96E17" "Q96E39" "Q96FJ2"
[419] "Q96HU8" "Q96I24" "Q96N66" "Q96QR8" "Q96RD7" "Q96T51" "Q99460" "Q99471" "Q99733" "Q99747" "Q9BPW8"
[430] "Q9BRX8" "Q9BTY2" "Q9BUH6" "Q9BVM2" "Q9BW30" "Q9BYD2" "Q9BYT8" "Q9BZ95" "Q9H0A8" "Q9H115" "Q9H1E3"
[441] "Q9H1K0" "Q9H3N1" "Q9H492" "Q9H4A6" "Q9H845" "Q9H8Y8" "Q9H910" "Q9H936" "Q9H9J2" "Q9HAS0" "Q9HAV4"
[452] "Q9HAV7" "Q9HCJ6" "Q9NQ48" "Q9NQ66" "Q9NRF8" "Q9NRX4" "Q9NSE4" "Q9NVI7" "Q9NX76" "Q9NXG6" "Q9NY47"
[463] "Q9NYL9" "Q9NZ53" "Q9NZ72" "Q9NZI8" "Q9P016" "Q9P032" "Q9P0J0" "Q9P0V9" "Q9P121" "Q9P1W3" "Q9P2E9"
[474] "Q9UBT2" "Q9UEL6" "Q9UEY8" "Q9UHB9" "Q9UHL4" "Q9UII2" "Q9UIV1" "Q9UIW2" "Q9UJS0" "Q9UK22" "Q9UKY7"
[485] "Q9ULH1" "Q9UMX5" "Q9UNN8" "Q9UQ80" "Q9Y277" "Q9Y2B0" "Q9Y2J2" "Q9Y2X3" "Q9Y3D6" "Q9Y3I0" "Q9Y490"
[496] "Q9Y512" "Q9Y5B9" "Q9Y5L4" "Q9Y5P6" "Q9Y678" "Q9Y680" "Q9Y6M1" "Q9Y6Y8"
# apply to filtered list
regulation_summary <- summarize_regulations(filtered_DEP)
print(regulation_summary)
Upregulated Downregulated
GBA-KO 109 137
IGSF9B-KO 559 738
INPP5F-KO 144 209
IP6K2-KO 59 87
PINK1-KO 274 496
PRKN-KO 373 573
SH3GL2-KO 133 116
SNCA-A53T 162 244
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO","PRKN-KO","IGSF9B-KO","INPP5F-KO","IP6K2-KO","IGSF9B-KO", "INPP5F-KO", "IP6K2-KO") # Specify the contrasts of interest
all <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)
print(all$`Overlapping Genes`)
names(filtered_DEP)
[1] "GBA-KO" "IGSF9B-KO" "INPP5F-KO" "IP6K2-KO" "PINK1-KO" "PRKN-KO" "SH3GL2-KO" "SNCA-A53T"
print(gba.pink1)
$`Overlapping Genes`
[1] "HDLBP" "EHD1" "OSTC" "VTA1" "NCAM1" "A0A0J9YYL3" "STXBP1" "A0A1P0AYU5" "HNRNPU" "RPL5" "RPS14"
[12] "SMARCE1" "A0A3B3IRQ9" "PALM3" "HSPA12A" "LMNA" "PRKRA" "P4HB" "OBSCN" "SNRPE" "EWSR1" "B4DLN1"
[23] "HNRNPC" "B4E171" "SCFD1" "CALD1" "CYCS" "CTSD" "RPS3A" "SNCA" "NEFM" "HNRNPH1" "TP53I11"
[34] "PRCP" "ATP6V0D1" "F8W6I7" "CD44" "H3BQZ7" "APRT" "ATXN2L" "HMGN1" "NCAM2" "RPL17" "CBX1"
[45] "DDX5" "SLC16A3" "SF3A2" "HNRNPUL1" "ACOT7" "C11orf58" "MAN2B1" "DPYSL4" "VGF" "SYT7" "DCX"
[56] "KPNA6" "CS" "PSIP1" "NDUFS3" "CLN5" "GLRX3" "SNCG" "CIAO1" "LY6H" "TOMM70" "DIRAS1"
[67] "HSPA4L" "TXNDC12" "GOT2" "CA2" "HRAS" "FUCA1" "ANXA1" "OAT" "ATP1B1" "HMGN2" "FABP3"
[78] "SSB" "KRT18" "PYGL" "NEFL" "ANXA2" "DCN" "FH" "ANXA6" "SYP" "VIM" "ANXA5"
[89] "GSTP1" "GNAO1" "HSPA5" "RALA" "XRCC6" "ATP1A3" "PDIA4" "P4HA1" "SNRPB" "HNRNPL" "EZR"
[100] "GNS" "PPP3CB" "NAGA" "SYN1" "GAP43" "ATP5PF" "NCL" "HK1" "LMNB1" "FLNA" "MAOA"
[111] "VDAC1" "HNRNPA2B1" "ATP2B4" "ATP5PB" "FKBP2" "CALR" "EPHB2" "EEF1D" "CRABP1" "PRDX6" "LRPAP1"
[122] "TSPO" "ALDH1B1" "GK" "RPS19" "PRPH" "ACAA2" "CBX5" "ALDH9A1" "FASN" "SRP9" "SERPINB9"
[133] "SERPINH1" "GPM6A" "HDGF" "STX1B" "RPS8" "RPS23" "RPS13" "RPL23A" "RPL23" "GNB1" "RPL10A"
[144] "ABAT" "ERH" "SET" "HMGCS1" "PLOD1" "NUCB1" "RPL6" "CRYZ" "LGALS3BP" "NCBP1" "AHNAK"
[155] "GALNT2" "FSTL1" "CNTN1" "DNAJC3" "TRIM28" "PDAP1" "ALCAM" "FLNC" "PLEC" "CNN3" "SNCB"
[166] "STX1A" "FNDC3B" "HMGB1" "GDAP1L1" "NXN" "FAHD1" "CEP68" "KTN1" "CEND1" "CADM2" "SYT2"
[177] "SERBP1" "PLBD2" "NUBPL" "CELF1" "GLG1" "ATP6V0A1" "CMBL" "RAB3C" "DIRAS2" "FUBP3" "RBM17"
[188] "SCN2A" "SEPTIN5" "NAPG" "DPYSL5" "FUCA2" "MAP1LC3A" "GOLPH3" "JPT2" "SLC25A22" "CTPS2" "PHPT1"
[199] "TMOD3" "SEPTIN10" "NTM" "RRBP1" "UBA2" "ADD3" "PLXNA1" "SLC25A13" "NENF" "THRAP3" "SAMM50"
[210] "SUPT16H" "SEC23IP"
$`Unique Genes`
$`Unique Genes`$`GBA-KO`
[1] "SNX12" "MADD" "A0A0G2JLB3" "C9JIZ6" "IDH3G" "SPARC" "NRXN2" "BAIAP2" "YES1" "PLXNC1" "SLC25A5"
[12] "SCG2" "ATP6V1B2" "COMT" "ITGA6" "MAOB" "SDHA" "RPL4" "PLCB3" "SELENBP1" "NNT" "ELAVL3"
[23] "QPRT" "RHEB" "MRPL23" "HSDL1" "RELL2" "ABCF1" "SMARCC2" "RAPGEF4" "RMDN1" "MAP7D2" "GDA"
$`Unique Genes`$`PINK1-KO`
[1] "A0A087WTM1" "PABPC1" "CLTC" "TPM3" "A0A087WY61" "TARDBP" "SRSF3" "CD99" "MYEF2"
[10] "MBNL1" "A0A0A0MR09" "NOLC1" "GSN" "ILK" "TMED7-TICAM2" "PON2" "ENAH" "QARS1"
[19] "SCARB2" "SEPTIN3" "RDX" "MEA1" "RPS24" "GLUL" "MOGS" "EML1" "TMEM132E"
[28] "PGM3" "DLG1" "AP3B2" "A0A5F9UP49" "ACAT1" "DDX17" "SCRIB" "TCOF1" "CAST"
[37] "GDAP1" "UBE3A" "TRIM2" "EIF3E" "ADAM9" "EIF4H" "NONO" "PPIG" "NPM1"
[46] "SYNCRIP" "PITRM1" "SRP54" "ACSL3" "SLC1A3" "DNAJC10" "RMND1" "HSD17B4" "RABGAP1L"
[55] "CRELD1" "LRPPRC" "IARS1" "SHTN1" "A2A2V1" "NUDT5" "PTBP1" "TP53BP1" "SUMO3"
[64] "MATR3" "HP1BP3" "PALM2AKAP2" "EIF3H" "MSI2" "NQO1" "ILF2" "PREB" "EIF3CL"
[73] "SH3GLB2" "CROT" "CTNND1" "PLRG1" "HNRNPAB" "SEC31A" "MRPS27" "CIRBP" "PYCR1"
[82] "SKP1" "SDC2" "UGP2" "EIF4B" "EIF4G1" "ALYREF" "AGK" "CELF2" "OGDH"
[91] "DPP6" "TNPO3" "ADCYAP1R1" "EPB41L2" "E9PJP2" "HSPA8" "UBTF" "RPL8" "PTPRD"
[100] "SLC3A2" "LAMTOR1" "CSRP2" "PCBP2" "UBAP2L" "F8WE88" "NDUFV1" "GRID1" "TJP1"
[109] "NPC2" "ARFGAP2" "H0Y3P2" "COPB2" "NACA" "RPL28" "H3BN98" "PDIA3" "BET1"
[118] "PTMA" "TBL3" "RPL36A" "TMEM199" "ZNF207" "SRSF1" "PRKCSH" "FXYD7" "FARSA"
[127] "KDSR" "GPX4" "RPS5" "RPL21" "RPS16" "RPL18A" "HIP1" "IGF2BP3" "GOLIM4"
[136] "HMGN4" "PIR" "LIN7A" "HNRNPDL" "DCLK1" "DHX15" "RNMT" "PHGDH" "NDUFS4"
[145] "DYNC1LI2" "PSMD3" "HNRNPR" "TPD52L2" "TGOLN2" "TIMM44" "CHMP2A" "NCK2" "BUB3"
[154] "AHCYL1" "SPAG9" "OPA1" "SNAP91" "PLIN3" "USO1" "PRAF2" "EIF5B" "NDUFS2"
[163] "SH3BGRL" "FLNB" "SEC22B" "KHDRBS3" "SF3B1" "CRTAP" "SLC25A12" "IDH1" "CPD"
[172] "SEC24D" "UFL1" "ABCA8" "GLS" "AP2A2" "RTN3" "FKBP9" "PGLS" "NTN1"
[181] "GSR" "COX2" "HP" "KRAS" "TFRC" "CSTB" "TUBB4A" "RPN1" "FGF1"
[190] "RPLP0" "PRKCB" "KRT8" "CALB1" "GPI" "ITGAV" "EPRS1" "CTSB" "HSP90AA1"
[199] "PFKM" "SNRPB2" "RPSA" "SNRPA" "PARP1" "LTA4H" "ALDOC" "RO60" "GAA"
[208] "H1-4" "CLU" "PYGB" "LAMP1" "G6PD" "PCNA" "NEFH" "CKMT1B" "CKMT1A"
[217] "XRCC5" "COX7A2" "HSP90B1" "DARS1" "ARSA" "RPS2" "CBR1" "GLB1" "ATP2A2"
[226] "CD36" "DES" "RPL35A" "ATP2B1" "ANXA7" "RAB3A" "PTMS" "ATP6V1C1" "SDHB"
[235] "SCP2" "IGFBP4" "SFPQ" "RPS3" "AHCY" "RPS12" "ATP5F1A" "PSMA3" "ITGB8"
[244] "DDX6" "CTNNA2" "RPL13" "EEF1G" "CNTFR" "RPL10" "MAP4" "GRN" "TKT"
[253] "PRDX5" "RPL12" "CORO1A" "DNAJA1" "HNRNPH3" "YWHAB" "L1CAM" "SLC8A1" "RPL9"
[262] "HSPA4" "CTNNA1" "NF2" "RPL22" "FUS" "ATP5F1C" "ATP6V1E1" "SRP14" "TALDO1"
[271] "GGCX" "ATP6V1A" "RPL3" "NNMT" "USP8" "MDH2" "RPL35" "PAFAH1B1" "MCAM"
[280] "CRAT" "NSF" "RPS9" "RPS10" "IQGAP1" "STT3A" "ARCN1" "LSS" "NES"
[289] "LMAN1" "ACADVL" "TMED10" "GMPS" "GNAQ" "ANXA11" "SSR4" "BCAP31" "CLCN7"
[298] "HNRNPA3" "HNRNPM" "RAP1GDS1" "GTF2A1" "SMS" "MRPL12" "HMGA2" "ACLY" "COPB1"
[307] "COPA" "ATP1B3" "RAD23B" "NAGLU" "PSMD4" "OXCT1" "MARS1" "EIF6" "H2BC5"
[316] "GNG2" "CD81" "TPI1" "EIF4A1" "RPS20" "SNAP25" "RAP1B" "RPL26" "NUTF2"
[325] "HNRNPK" "RPS7" "PPP1CB" "RPS18" "RPS11" "SNRPG" "SNRPD1" "SNRPD3" "RPL7A"
[334] "RPS4X" "ACTA2" "RHOB" "VSNL1" "RPL30" "RPL31" "GNAI1" "RPS21" "RACK1"
[343] "TPM4" "EEF1A1" "TUBA4A" "PAFAH1B2" "CXADR" "CNTNAP1" "PRKDC" "NUCB2" "BASP1"
[352] "RPL24" "PURA" "FKBP3" "DR1" "TOP2B" "ACY1" "LMNB2" "DNM1" "EEF1A2"
[361] "PSME1" "CKAP4" "KHDRBS1" "PPP3CA" "DHX9" "MFGE8" "SF3A3" "ILF3" "LMAN2"
[370] "CBX3" "SRSF9" "PABPC4" "OS9" "TUBB3" "CUL3" "EIF3A" "MVP" "GOLGB1"
[379] "SEPTIN2" "PAFAH1B3" "RAB35" "TMED2" "PCBP1" "ELOC" "SF3B3" "SAFB" "SHH"
[388] "MAPRE2" "HMGN3" "EFNB3" "SEPTIN7" "ADRM1" "INA" "DDB1" "ATP2B3" "H2AC20"
[397] "H2BC21" "P3H1" "LSM12" "PREPL" "XKR4" "MIA3" "FHL1" "ATAT1" "CEP170"
[406] "ODR4" "SF3B4" "Q5T0I0" "FKBP15" "NBEA" "SRSF11" "LSM14B" "HEXB" "CD276"
[415] "MRPL14" "PRPF8" "PLPPR3" "NSMF" "PPP1R21" "TUBA1A" "SND1" "TRMT10C" "DGLUCY"
[424] "COMMD6" "NECAB2" "PRRT2" "CENPV" "PRUNE1" "SETD3" "NT5DC3" "MB21D2" "CCAR2"
[433] "GPD1L" "ARFGAP1" "ARL10" "GATD1" "ATAD1" "LEMD2" "NUP43" "CMAS" "NPLOC4"
[442] "PLPPR1" "STT3B" "BRSK1" "HNRNPLL" "SCG3" "CTNNBL1" "HTRA1" "HDAC2" "NEO1"
[451] "KHSRP" "MYDGF" "ERGIC1" "SLC25A46" "PBXIP1" "VPS33A" "FKBP10" "FAF2" "NTNG2"
[460] "DNAJC19" "RBMXL1" "DYNLL2" "VMP1" "MAP6" "MBOAT7" "VPS35" "PURB" "PANX1"
[469] "RUFY1" "PSMD1" "PFDN5" "NAP1L4" "NIPSNAP1" "PRXL2A" "ERP44" "FSD1" "PAXX"
[478] "DPCD" "TPPP3" "MRPL9" "NLN" "NSD3" "API5" "NAT10" "COMMD4" "NAPB"
[487] "NUCKS1" "RBSN" "EHD4" "TMX1" "RPAP3" "ACAD9" "GORASP2" "MRPL44" "C17orf75"
[496] "XPO5" "GRPEL1" "VAT1L" "PLXNA4" "LZTFL1" "PLCB1" "GPHN" "FARSB" "IARS2"
[505] "ATAD3A" "SLTM" "CMTM6" "P4HTM" "CACNA2D2" "SMPD3" "PODXL2" "STMN3" "IGF2BP1"
[514] "THYN1" "NDUFAF4" "NDUFA13" "RAI14" "MACROH2A2" "TMEM63C" "GNG12" "HDAC6" "MPZL1"
[523] "SRP68" "DPP7" "NRBP1" "ATP5IF1" "CNOT7" "FBXO2" "CDV3" "ASAP1" "CADPS"
[532] "PROCR" "PHF24" "DNM3" "PA2G4" "VDAC3" "CNPY2" "WDR37" "EPB41L3" "NOP58"
[541] "LSM2" "SF3B6" "FIS1" "RTCB" "TLN1" "DAAM1" "ATP6V1D" "TIMM13" "GMPPB"
[550] "COPG1" "FKBP7" "IGF2BP2" "ERC1"
# bright genome overlap
contrast_list <- c("IGSF9B-KO", "INPP5F-KO","IP6K2-KO","SH3GL2-KO") # Specify the contrasts of interest
result.dark <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)
# Print results
print(result.dark$`Overlapping Genes`) # Genes common across all specified contrasts
[1] "PCP4L1" "SYNM" "CSPG5" "UTS2" "TH" "CSRP1" "VAMP1" "MAOB" "NOVA1" "GPM6A" "DYNLT3" "EPHB1" "SNCB" "CARTPT" "PDK4"
[16] "LBH" "SV2A" "FAM162A" "LSM14B" "SLC6A17" "PSD3" "NTM" "SLC17A6" "GNG12" "SUN2" "PHF24"
#print(result.dark$`Unique Genes`) # List of genes unique to each contrast
Look at overlap in contrasts that also have targeted pathways changes that match
print(result.THdown$`Overlapping Genes`) # Genes common across all specified contrasts
[1] "EPHB1" "PSD3" "PYCR1" "NCAM2" "SLC16A3" "RTN3" "HP" "FTL" "HSPB1" "TH" "ALDOC" "MAOA" "MAOB"
[14] "CALR" "ACLY" "CACNA2D1" "HADHB" "RAP1B" "VSNL1" "ALCAM" "COTL1" "SV2A" "CNDP2" "SYT4" "CACNA2D2" "NTM"
[27] "RRBP1" "FBXO2"
plot_protein_heatmap_zscore(
data = df,
proteins = result.THdown$`Overlapping Genes`, # Example protein names
sample_patterns = c("Control","PRKN.KO","IGSF9B" ,"SH3GL2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
scale_values = c(-1, -0.5,-0.25, 0, 1, 2.5, 4.5), # Adjust based on your data range
group_means = TRUE,# Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
Function to plot grouped by expression
# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
data = df,
proteins = result.THdown$`Overlapping Genes`, # Example protein names
sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values =c(-1, -0.5,-0.25, 0, 1, 2.5, 4.5), # Adjust based on your data range
group_means = FALSE, # Set to FALSE if you want individual samples
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
data = df,
proteins = result.THdown$`Overlapping Genes`, # Example protein names
sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5,-0.25, 0, 1, 2.5, 4.5), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
cell_width = 20, # Control column width
cell_height = 10 # Control row height
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
data = df,
proteins = result.THdown$`Overlapping Genes`, # Example protein names
sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1, -0.5,-0.25, 0, 1, 2.5, 4.5), # Adjust based on your data range
group_means = FALSE, # Set to FALSE if you want individual samples
cell_width = 20, # Control column width
cell_height = 10 # Control row height
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
contrast_list <- c("PRKN-KO","IGSF9B-KO","SH3GL2-KO", "IP6K2-KO") # Specify the contrasts of interest
result.GCasedown <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)
# Print results
print(result.GCasedown$`Overlapping Genes`) # Genes common across all specified contrasts
[1] "EPHB1" "PSD3" "TH" "MAOB" "CACNA2D1" "SV2A" "NTM"
Overlapping list of the genotypes with GCAse activity down: GBA-KO, PRNK-KO, INPP5F-KO, SH3GL2-KO, IP6K2-KO
# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
data = df,
proteins = result.GCasedown$`Overlapping Genes`, # Example protein names
sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1.5, -1,-0.5, 0, 1, 2, 2.5), # Adjust based on your data range
group_means = FALSE, # Set to FALSE if you want individual samples
cell_width = 20, # Control column width
cell_height = 10 # Control row height
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, IP6K2.KO.1, IP6K2.KO.2, IP6K2.KO.4, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
pro.list <- result.lyso$`Overlapping Genes`
# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
data = df,
proteins = pro.list, # Example protein names
sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-0.5,-0.25, 0, 2.5, 4.5, 6.5), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
cell_width = 20, # Control column width
cell_height = 10 # Control row height
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, IP6K2.KO.1, IP6K2.KO.2, IP6K2.KO.4, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
data = df,
proteins = pro.list, # Example protein names
sample_patterns = c("Control", "IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-0.5,-0.25, 0, 2.5, 4.5, 6.5), # Adjust based on your data range
group_means = FALSE, # Set to FALSE if you want individual samples
cell_width = 20, # Control column width
cell_height = 10 # Control row height
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, IP6K2.KO.1, IP6K2.KO.2, IP6K2.KO.4, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
lysome <- read_excel("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/List of genes for RNAseq or Proteomics/LYSOSOME_GENE LIST.xlsx")
Gene list
plot_protein_heatmap_zscore(
data = df,
proteins = lysome$`Gene name`, # Example protein names
sample_patterns = c("Control","INPP5F.KO","SH3GL2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1","red", "red2", "firebrick4"),
scale_values = c(-1, -0.5,-0.25, 0, 1, 2.5, 5.5), # Adjust based on your data range
group_means = TRUE,# Set to FALSE if you want individual samples
tile_width = 0.25
)
[1] "Sample columns selected: Control.1, Control.2, Control.3, Control.4, INPP5F.KO.1, INPP5F.KO.2, INPP5F.KO.3, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
get overlap lists
colnames(df)
[1] "Symbol" "Control.1" "Control.2" "Control.3" "Control.4" "IGSF9B.KO.1" "IGSF9B.KO.2" "INPP5F.KO.1" "INPP5F.KO.2" "INPP5F.KO.3"
[11] "IP6K2.KO.1" "IP6K2.KO.2" "IP6K2.KO.4" "SH3GL2.KO.1" "SH3GL2.KO.2" "SH3GL2.KO.3"
contrast_list <- c("INPP5F-KO","SH3GL2-KO","IP6K2-KO","IGSF9B-KO") # Specify the contrasts of interest
dark.ol <- get_gene_overlap_and_unique(filtered_DEP, contrast_list)
print(dark.ol)
$`Overlapping Genes`
[1] "PCP4L1" "SYNM" "CSPG5" "UTS2" "TH" "CSRP1" "VAMP1" "MAOB" "NOVA1" "GPM6A" "DYNLT3" "EPHB1" "SNCB" "CARTPT" "PDK4"
[16] "LBH" "SV2A" "FAM162A" "LSM14B" "SLC6A17" "PSD3" "NTM" "SLC17A6" "GNG12" "SUN2" "PHF24"
$`Unique Genes`
$`Unique Genes`$`INPP5F-KO`
[1] "PIGBOS1" "CLIC1" "STX16" "U2SURP" "NACAD" "XPOT" "STX6" "SIN3B" "DAB1" "TUSC2" "ACTL6B" "ZRANB2" "BAG3" "CFB" "ASS1"
[16] "P01861" "APOH" "ALB" "TF" "HPX" "FUCA1" "S100B" "SNRNP70" "SLC2A1" "SCG2" "UBTF" "POLR2E" "PPIB" "AK4" "CRABP1"
[31] "ERP29" "GCHFR" "SHMT2" "GARS1" "NSG1" "SLC1A4" "SMS" "SUB1" "MANF" "TRA2B" "NUCB2" "SRSF3" "SRSF2" "PPARD" "CD47"
[46] "FLII" "SRSF9" "SRSF6" "SQSTM1" "PRP4K" "IDI1" "SAFB2" "SMC1A" "CHD4" "RNPS1" "TERF2" "PTPRN" "MAP7D1" "HP1BP3" "UBR4"
[61] "ARFGEF3" "CD276" "ARSK" "ARMCX2" "NDUFAF7" "MICALL1" "PRUNE2" "COPS9" "KCTD12" "RBM17" "PPP1R9B" "CNN2" "SF3B5" "C1QTNF4" "SPRY4"
[76] "PNN" "GHITM" "EHMT1" "GMPR2" "TAGLN3"
$`Unique Genes`$`SH3GL2-KO`
[1] "AIP" "GTPBP1" "MGRN1" "LY6H" "CNTN5" "KBTBD11" "SLC25A4" "POLR2C" "ATP2B1" "GLRX" "ALDH9A1" "ALDH7A1" "BCAM"
[14] "HSD17B4" "KPNA1" "TPD52" "NCALD" "PAFAH1B2" "GPM6B" "GRM7" "ETFDH" "FRY" "TBC1D9B" "ZC3HAV1" "ARHGAP12" "GDPD1"
[27] "NUDT10" "ITFG1" "MICAL1" "MYDGF" "SH3KBP1" "CHAMP1" "LYSMD1" "TSPAN18" "HSD17B10" "OLFM1" "DNAJC5" "HEBP1" "ABCB8"
[40] "TMEM30A" "GDE1" "GPRC5B" "EHD3" "CACFD1" "SEPTIN9" "GRID1" "NFU1" "GAB2" "COPG1"
$`Unique Genes`$`IP6K2-KO`
[1] "PDLIM1" "EDIL3" "BRD4" "WDR1" "PRKCB" "NEFH" "PRKCA" "PLCG1" "ITGA6" "NF2" "FDFT1" "MTIF2" "DLG4" "EPS8" "RAPGEF1"
[16] "CIRBP" "FKBP8" "CYP51A1" "PRPF40B" "SUGP2" "SCAI" "SYNE1" "PLPPR1" "FBXO41" "SPOCK2" "EVI5L" "FMNL2" "FYTTD1" "CAMK2N2" "SRCIN1"
[31] "NT5DC2" "SERINC1" "RPRM" "TES" "OGDHL" "TDRKH" "TMX2"
$`Unique Genes`$`IGSF9B-KO`
[1] "ESYT2" "SHTN1" "ILVBL" "DENND3" "PALM3" "CASTOR2"
[7] "FAM171A2" "MCRIP1" "SLC35A4" "DNASE2" "KIF2A" "ACOT7"
[13] "MYO1C" "DFFA" "RTCA" "MANBA" "SDCBP" "RNASET2"
[19] "PODXL" "MAN2B1" "PDXK" "ARID1A" "SDHD" "DPYSL4"
[25] "COX7A2L" "UBFD1" "ABLIM1" "ADAM10" "EI24" "TP53I11"
[31] "PDCD5" "TPP1" "CPLX1" "PSMA7" "LIN7A" "TIMM23"
[37] "CASK" "UQCRQ" "CLGN" "SYN3" "SPTBN2" "KIF3B"
[43] "DCLK1" "SNPH" "DEGS1" "SCAMP2" "PGRMC2" "PFDN6"
[49] "RER1" "SURF4" "SPTLC1" "HMGB3" "STX7" "YKT6"
[55] "FABP7" "RNMT" "SEPTIN4" "DYNC1LI2" "SRGAP3" "TXNL1"
[61] "TPD52L2" "SYNJ1" "PROM1" "TGOLN2" "DENR" "DCX"
[67] "NCK2" "TSPAN6" "ZNF207" "NDUFB5" "NDUFB3" "AP1G1"
[73] "SGTA" "SLC25A20" "NUDT21" "CALU" "EXTL3" "MFSD11"
[79] "SPAG9" "KIF5C" "ACSL4" "SYNCRIP" "SELENOF" "TSPAN2"
[85] "TSPAN3" "SNAP91" "DPM1" "TIMM17B" "PRAF2" "CUTA"
[91] "PFDN1" "ABCB7" "SRGAP2" "DNAJC6" "ATP9A" "CLASP2"
[97] "PPFIA3" "TBCA" "MPDU1" "MACROH2A1" "PEX14" "NDUFB1"
[103] "ERLIN1" "CLN5" "HSBP1" "BANF1" "TIPRL" "PPM1B"
[109] "ADAP1" "PALM" "ATP6AP2" "EIF3G" "EIF3J" "ZMPSTE24"
[115] "IDH1" "ATRN" "DGAT1" "PAK3" "ARL6IP5" "FLOT1"
[121] "TRIO" "ATP5MG" "GLRX3" "RSL1D1" "SNCG" "DDAH1"
[127] "B3GAT3" "DDHD2" "NFASC" "TMCC1" "ERLIN2" "ENDOD1"
[133] "GLS" "WDR47" "AP2A2" "CLSTN1" "YIF1A" "NDUFB4"
[139] "SBF1" "VAPB" "SNAPIN" "NDUFC2" "FKBP9" "PGLS"
[145] "SEC24A" "OXSR1" "GGPS1" "BAG2" "DDAH2" "TXNDC12"
[151] "NDUFB10" "TOMM40" "PEX11B" "COX2" "HPRT1" "AK1"
[157] "ATP6" "HRAS" "IGF2" "LMNA" "APOE" "APOC3"
[163] "TFRC" "FTH1" "GBA1" "ALDOA" "CSTB" "OAT"
[169] "TUBB4A" "HLA-A" "RPN1" "RPN2" "PCCA" "PCCB"
[175] "SCG5" "ATP5MC1" "ITGB1" "GLA" "PTMA" "GPI"
[181] "TPM3" "HEXA" "EPHX1" "LDHB" "NEFL" "NEFM"
[187] "P4HB" "H1-0" "ACYP1" "CTSD" "ANXA2" "TUBB"
[193] "PSAP" "HEXB" "CTSB" "LAMB1" "ANXA6" "SYP"
[199] "ANXA5" "SNRPA" "ENO2" "GNAO1" "CLTA" "ANXA4"
[205] "COX6C" "UCHL1" "SMIM13" "RAP2A" "GAA" "H1-4"
[211] "TXN" "CTSA" "MAPT" "CHGA" "HSPD1" "CLU"
[217] "HSPA5" "ACP2" "MAP2" "PDHB" "DBT" "RALA"
[223] "RALB" "LAMP1" "TOP2A" "G6PD" "UBL4A" "IGF2R"
[229] "PCNA" "COL6A1" "CKMT1B" "CKMT1A" "ACTN1" "PEPD"
[235] "LAMP2" "NCAM1" "ATP1A3" "P4HA1" "PRKAR2A" "MIF"
[241] "FDPS" "COX7A2" "PKM" "HNRNPL" "UQCRB" "AKR1B1"
[247] "ARSA" "UCHL3" "CD46" "GNS" "ARSB" "RPA2"
[253] "COX7C" "GLB1" "PPP3CB" "H1-5" "H1-3" "H1-2"
[259] "CPE" "STMN1" "NAGA" "GOT1" "NDUFB7" "SYN1"
[265] "DES" "GAP43" "GM2A" "SON" "GNAZ" "ANXA7"
[271] "RAB3B" "PTMS" "GSTM3" "ATP6V1B2" "ATP6V1C1" "CNR1"
[277] "SYT1" "VDAC1" "OSBP" "PCMT1" "FBL" "FDXR"
[283] "PRKACB" "UQCRC2" "GCSH" "PTPRD" "CFL1" "EEF1B2"
[289] "TNC" "GRK2" "PSMA1" "PSMA3" "PSMA4" "DDX6"
[295] "CNTFR" "YWHAQ" "MARK3" "MAP4" "CANX" "PSMA5"
[301] "PSMB6" "PSMB5" "TMOD1" "GRN" "LAP3" "IMPA1"
[307] "EPHB2" "CMPK1" "PEBP1" "CORO1A" "GDI1" "PRKAR2B"
[313] "TIA1" "UQCRC1" "YWHAB" "STIP1" "S100A11" "L1CAM"
[319] "PRDX2" "GALNS" "SDC2" "HSPA4" "PFN2" "CTNNA1"
[325] "PHB1" "MYH9" "MYH10" "ADD1" "ADD2" "BSG"
[331] "PPM1A" "HMGCL" "P36268" "ARL3" "DLST" "GPX4"
[337] "NUP62" "SNCA" "LIPA" "ATP6V1A" "COL18A1" "MDH1"
[343] "HADHA" "CETN2" "CD200" "ACTR1B" "EPS15" "CASP3"
[349] "ACAA2" "RPL35" "PRCP" "ECE1" "PAFAH1B1" "SSR1"
[355] "VDAC2" "CRK" "CRKL" "NSF" "MAP1B" "STT3A"
[361] "RAP1GAP" "CAPZB" "UQCRFS1" "TFPI2" "PIP4K2A" "PPP3CC"
[367] "CD151" "NES" "HSPA13" "MARCKSL1" "LMAN1" "AMPH"
[373] "INPP1" "HARS2" "PSMB3" "ACADVL" "TMED10" "PSEN1"
[379] "TSC2" "GSK3B" "NT5C2" "SEPHS1" "GDI2" "CPT1A"
[385] "SERPINB9" "ST13" "ANXA11" "SSR4" "HCFC1" "ALDH5A1"
[391] "PSMD7" "HDGF" "PGD" "RAP1GDS1" "GTF2A1" "KIF11"
[397] "HMGA2" "PPP5C" "MVD" "CTSC" "PTTG1IP" "SLC16A1"
[403] "RAD23A" "RAD23B" "ALDH18A1" "MFAP2" "AFDN" "CDH6"
[409] "HNRNPH2" "BID" "ARPP19" "AP1S2" "CORO7" "EPPK1"
[415] "FXYD7" "TPI1" "SEC61B" "PSMA6" "S100A10" "SPCS3"
[421] "UBE2K" "UBE2N" "RPL26" "STX1B" "RPL27" "PCBD1"
[427] "SEC61A1" "VBP1" "STXBP1" "DAD1" "NPC2" "PKIA"
[433] "SUMO2" "UFM1" "AP1S1" "YWHAG" "YWHAE" "RPS18"
[439] "RPS13" "ARF6" "PPP2CB" "RHOB" "RPS6" "RPS24"
[445] "GNB1" "RBX1" "RPL32" "PPIA" "FKBP1A" "RAC1"
[451] "VAMP2" "YWHAZ" "PPP2CA" "YBX1" "SEC11A" "UBE2L3"
[457] "TUBA4A" "TUBB4B" "CXADR" "GTF2I" "PIP4K2B" "SLC35A1"
[463] "ARG2" "BASP1" "MRPS11" "ARF5" "H3-3A" "H3-3B"
[469] "HSPG2" "CYCS" "SLC25A3" "CDK5" "CLTC" "TIAL1"
[475] "SET" "AMPD2" "CTBS" "CAP1" "HMGCS1" "DR1"
[481] "ATP2B2" "EWSR1" "OCRL" "PLOD1" "RPL6" "SLC25A11"
[487] "PTS" "MVK" "EEF1A2" "PSME1" "PRDX1" "CKAP4"
[493] "TJP1" "KLC1" "LRP1" "ARHGAP1" "PPP3CA" "GOLGA3"
[499] "LGALS3BP" "MFGE8" "DMTN" "PPID" "SCRN3" "GALNT2"
[505] "AP1B1" "KIF1A" "SCRN1" "CNTN1" "LMAN2" "ANK3"
[511] "PAK1" "PAK2" "DNAJC3" "NME3" "MAD2L1" "PTK7"
[517] "AP3B2" "UBE2V1" "PEDS1-UBE2V1" "DYNC1I2" "OS9" "PDAP1"
[523] "TMED1" "LSAMP" "MTX1" "TUBB3" "CAMK2B" "DCTN2"
[529] "STIM1" "ITGA7" "MOGS" "SPTAN1" "TUBB2A" "HNRNPD"
[535] "SCARB2" "DAG1" "MLEC" "TTLL12" "MPP2" "CRMP1"
[541] "DPYSL3" "DCTN1" "EIF4A2" "FLOT2" "FLNC" "ELAVL3"
[547] "INPP5A" "CLINT1" "GANAB" "LBR" "MVP" "SPCS2"
[553] "EMC2" "PSMD6" "SEPTIN2" "RRS1" "POSTN" "PDIA6"
[559] "PAFAH1B3" "PPP2R5B" "PTPA" "QPRT" "RABEP1" "RCN1"
[565] "TMED2" "SHH" "MAPRE2" "SF1" "MAPRE1" "EFNB3"
[571] "RAB30" "ITSN1" "TBCE" "UBE2V2" "VAMP3" "NEDD8"
[577] "INA" "CSRP2" "DPYSL2" "STX1A" "CPSF6" "SMN2"
[583] "SMN1" "DBN1" "FSCN1" "ATP2B3" "TST" "HAGH"
[589] "H2BC21" "PTPRO" "UGP2" "HNRNPUL2" "P3H1" "LSM12"
[595] "CCDC88A" "LGALSL" "VPS26B" "CCDC184" "TMEM35A" "HSD17B12"
[601] "RAB6D" "SDK2" "TMEM97" "YIF1B" "MEST" "XKR4"
[607] "NOMO2" "MIA3" "WDR44" "GNAS" "SAMD4B" "ATAT1"
[613] "CEP170" "CLVS2" "TPRG1L" "FNBP1L" "GPR158" "WLS"
[619] "Q5TF21" "MICOS10" "WASHC2A" "PPP2R2D" "MBLAC2" "LMBRD2"
[625] "IQSEC1" "CARMIL2" "RALGAPA1" "TMEM132E" "TCEAL6" "SLC25A24"
[631] "REEP3" "ARMC6" "JMJD6" "PPP1R18" "SLC48A1" "SLC27A4"
[637] "EDC4" "SCYL2" "CNNM4" "ERICH5" "MEAK7" "PGM2L1"
[643] "AAGAB" "TMEM65" "NCEH1" "CPLX2" "MOXD1" "CYP20A1"
[649] "POGLUT2" "TMEM205" "ISLR2" "APOOL" "PACS1" "RAB11FIP1"
[655] "HSDL2" "CRACD" "GPRIN3" "TOM1L2" "TMTC3" "IKBIP"
[661] "UBE2R2" "TLCD3B" "TUBA1A" "RUFY3" "EPM2AIP1" "TAOK1"
[667] "MOB1B" "CHMP1B" "MICAL3" "GPRIN1" "NUP54" "KIF21A"
[673] "PRRT2" "TMED4" "GALNT7" "PRUNE1" "MAGI2" "USP48"
[679] "NT5DC3" "CAND1" "OSTM1" "DDX42" "NDUFA11" "DOLPP1"
[685] "GPSM1" "ERC1" "NUDCD3" "SLC44A2" "BRSK2" "SULF2"
[691] "DNAJC10" "RHOT2" "SIRT2" "NRM" "SMAP1" "KIAA0319L"
[697] "CACNA2D3" "CEND1" "NUP93" "ABHD12" "SLC43A2" "CADM2"
[703] "MMGT1" "AFAP1" "CALHM5" "KCNRG" "JAGN1" "COMMD1"
[709] "EMC1" "AMER2" "GATD1" "GOLM1" "COLGALT1" "SUMF2"
[715] "TMEM87A" "TXNDC5" "LEMD2" "NECAP1" "CAMKV" "PLA2G15"
[721] "Q8NCU8" "MROH1" "EHBP1" "MCU" "NUP37" "NBEA"
[727] "NLGN2" "CADM4" "PLBD2" "GRPEL2" "FGFBP3" "UBA3"
[733] "C18orf32" "STT3B" "HM13" "GPX8" "RAPGEF6" "C16orf78"
[739] "PDCD6IP" "PSPC1" "PPM1E" "JDP2" "LZIC" "IRGQ"
[745] "DDX1" "H1-10" "NCSTN" "TM9SF4" "WASF1" "SLC9A6"
[751] "NDRG1" "NUP205" "TTC9" "SORL1" "ARPC1A" "GGH"
[757] "NRCAM" "KIFAP3" "CELF1" "GLG1" "PTPRN2" "KHSRP"
[763] "USP9X" "STMN2" "BORCS5" "UBE2E3" "NCLN" "ERGIC1"
[769] "CCDC47" "TMEM230" "FUBP1" "VTI1A" "MCUR1" "FKBP10"
[775] "TOMM6" "ARL8A" "CCDC127" "EFHD2" "PYCR2" "DCPS"
[781] "PPP1R14B" "ISOC1" "FOXRED1" "AP2M1" "RAB39B" "CMBL"
[787] "ERLEC1" "RAB3C" "DAZAP1" "SGTB" "CYFIP2" "CNRIP1"
[793] "LINGO1" "DYNLL2" "OTUB1" "CHMP6" "CERS2" "ZC2HC1A"
[799] "CRELD1" "SFRP2" "DIRAS2" "CDK5RAP3" "TMX3" "FAM210B"
[805] "PHACTR3" "PRRC1" "GDAP1L1" "VSTM2L" "MBOAT7" "RUNDC3B"
[811] "NAP1L5" "AGAP3" "MIA2" "NEDD4L" "VPS35" "MAGI1"
[817] "PANX1" "PIGS" "SLC9A7" "YME1L1" "SCN2A" "TBCB"
[823] "SEC62" "PFDN5" "PARK7" "TSC22D3" "TTC1" "PHB2"
[829] "ATXN2" "SEPTIN5" "SIGMAR1" "NAPG" "EBNA1BP2" "H2BC13"
[835] "DPYSL5" "ECSIT" "SYT3" "TXNDC17" "NUDT16L1" "SDF4"
[841] "ERP44" "ESYT1" "TMEM43" "FSD1" "FUCA2" "LMF2"
[847] "TUBB2B" "TMEM109" "PBDC1" "PTDSS2" "TMED9" "TPPP3"
[853] "ACAT2" "CHID1" "FSD1L" "AP1M1" "RAB34" "TRIM2"
[859] "SEMA4C" "TTYH3" "YIPF3" "NDEL1" "ROGDI" "PITHD1"
[865] "MAP1LC3B" "UBA5" "NAT10" "KLC2" "FXYD6" "HDHD2"
[871] "MAGT1" "LMAN2L" "NAPB" "SIL1" "NUCKS1" "SH3BGRL3"
[877] "SLC38A1" "CPVL" "DPAGT1" "POFUT1" "PIGU" "MAP1LC3A"
[883] "CLSTN2" "EPB41L1" "SMDT1" "PRR36" "REEP1" "SFXN1"
[889] "COG4" "NMNAT1" "C17orf75" "TMEM165" "GLOD4" "PCDH9"
[895] "MCCC2" "TM9SF3" "APMAP" "VTA1" "NPHS2" "DYNLRB1"
[901] "EMC7" "PLCB1" "PFDN4" "EIF2B3" "OSTC" "RAB6B"
[907] "TOMM22" "LRRC4B" "RBM12" "STAU2" "SHFL" "TMEM106B"
[913] "AGPAT5" "CYRIB" "EXOC1" "TMEM38B" "ANO10" "NDUFB11"
[919] "OCIAD1" "CHCHD3" "P4HTM" "BABAM2" "DPP3" "SCN3A"
[925] "SCN3B" "TERF2IP" "TMOD3" "UGGT1" "ERAP1" "PODXL2"
[931] "TMOD2" "CHMP5" "HACD3" "KCMF1" "RAI14" "MACROH2A2"
[937] "TBC1D7" "TBC1D7-LOC100130357" "TOMM7" "TMEM63C" "CAMSAP3" "NRXN2"
[943] "DPM3" "NCDN" "EPS15L1" "MRC2" "CPNE7" "EXTL2"
[949] "DNAJB11" "SEL1L" "PEF1" "ZMYM2" "SLC25A10" "CLIP2"
[955] "CFDP1" "SEC63" "SEPTIN3" "CHORDC1" "UBQLN2" "PCYOX1"
[961] "PFDN2" "PUF60" "NRBP1" "PLXNA1" "GGT7" "NAGK"
[967] "SH3BGRL2" "SLC25A13" "DBNL" "PURG" "GGA1" "STOML2"
[973] "JPT1" "PTBP2" "TNIK" "PITPNC1" "MAN1B1" "CDV3"
[979] "NRXN1" "SLC39A10" "ASAP1" "CORO1C" "HPCAL4" "MYO6"
[985] "UBQLN1" "SNX12" "SYNRG" "SSR3" "FAF1" "MINPP1"
[991] "NOVA2" "NSFL1C" "MAPK8IP3" "USP24" "AGTPBP1" "MAPRE3"
[997] "CORO2B" "DNM3" "NUDC" "CFL2"
[ reached getOption("max.print") -- omitted 35 entries ]
specify group function to get the specific overlap list in the upset plot
print(overlap_genes)
[1] "EHD1" "B4DLN1" "CALD1" "ATP6V0D1" "APRT" "ATXN2L" "CS" "PSIP1" "NDUFS3" "TOMM70" "GOT2" "HMGN2" "FH"
[14] "VIM" "ATP5PF" "HK1" "ATP5PB" "ALDH1B1" "GK" "CBX5" "FSTL1" "PLEC" "HMGB1" "NXN" "FAHD1" "SERBP1"
[27] "NUBPL" "FUBP3" "JPT2" "SLC25A22" "PHPT1" "SEPTIN10" "UBA2" "ADD3" "NENF" "SUPT16H"
length(overlap_genes)
[1] 36
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/GBA_PINK1_PRKN_DPE.csv")
Make list corresponding to the upset plot to save and send to Roxanne
# pink parkin
contrast_list <- c("PINK1-KO", "PRKN-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of PINK1 PRKN")
[1] "Overlap of PINK1 PRKN"
print(overlap_genes)
[1] "PABPC1" "RDX" "MEA1" "A0A5F9UP49" "ACAT1" "SCRIB" "CAST" "EIF3E" "ADAM9" "EIF4H" "PITRM1"
[12] "SRP54" "ACSL3" "LRPPRC" "A2A2V1" "NUDT5" "TP53BP1" "SUMO3" "MATR3" "EIF3H" "MSI2" "NQO1"
[23] "EIF3CL" "SH3GLB2" "CROT" "CTNND1" "MRPS27" "EIF4B" "EIF4G1" "AGK" "OGDH" "EPB41L2" "SLC3A2"
[34] "PCBP2" "NDUFV1" "H0Y3P2" "COPB2" "NACA" "H3BN98" "TBL3" "KDSR" "IGF2BP3" "PIR" "DHX15"
[45] "NDUFS4" "PSMD3" "TIMM44" "BUB3" "OPA1" "USO1" "EIF5B" "NDUFS2" "SH3BGRL" "KHDRBS3" "NTN1"
[56] "GSR" "PARP1" "LTA4H" "RO60" "XRCC5" "CBR1" "ATP2A2" "CD36" "SDHB" "RPS12" "ATP5F1A"
[67] "RPL13" "EEF1G" "TKT" "PRDX5" "DNAJA1" "RPL9" "ATP5F1C" "SRP14" "TALDO1" "RPL3" "MDH2"
[78] "CRAT" "IQGAP1" "ARCN1" "GMPS" "BCAP31" "CLCN7" "MRPL12" "PSMD4" "OXCT1" "H2BC5" "CD81"
[89] "TPM4" "PRKDC" "PURA" "PABPC4" "EIF3A" "PCBP1" "ELOC" "SAFB" "HMGN3" "ADRM1" "DDB1"
[100] "PREPL" "ODR4" "FKBP15" "SRSF11" "NSMF" "TRMT10C" "COMMD6" "SETD3" "ARL10" "ATAD1" "NPLOC4"
[111] "SCG3" "NEO1" "VPS33A" "NTNG2" "DNAJC19" "PURB" "RUFY1" "PSMD1" "NAP1L4" "NIPSNAP1" "PRXL2A"
[122] "PAXX" "DPCD" "NLN" "NSD3" "COMMD4" "RBSN" "TMX1" "ACAD9" "GORASP2" "MRPL44" "XPO5"
[133] "GRPEL1" "LZTFL1" "IARS2" "ATAD3A" "IGF2BP1" "THYN1" "NDUFAF4" "NDUFA13" "MPZL1" "SRP68" "CNOT7"
[144] "PA2G4" "VDAC3" "CNPY2" "NOP58" "FIS1" "TIMM13" "IGF2BP2"
length(overlap_genes)
[1] 150
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PINK1_PRKN_DPE.csv")
print("saved")
[1] "saved"
# PRKN IGSF9B
contrast_list <- c("IGSF9B-KO", "PRKN-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B PRKN")
[1] "Overlap of IGSF9B PRKN"
print(overlap_genes)
[1] "RTCA" "PDXK" "CASK" "SYN3" "SURF4" "DENR" "ACSL4" "ZMPSTE24" "ATP5MG" "DDAH1" "ERLIN2" "SEC24A" "DDAH2"
[14] "AK1" "ALDOA" "PCCA" "TUBB" "HSPD1" "PDHB" "ACTN1" "PEPD" "FDPS" "UQCRB" "CPE" "CNR1" "PCMT1"
[27] "PRKACB" "UQCRC2" "PSMA4" "PSMA5" "PSMB6" "CMPK1" "PEBP1" "PRDX2" "PHB1" "ADD2" "ARL3" "VDAC2" "CRK"
[40] "CRKL" "UQCRFS1" "PSMB3" "GDI2" "PGD" "PPP5C" "CDH6" "PSMA6" "SPCS3" "UBE2K" "VBP1" "PPP2CB" "FKBP1A"
[53] "YBX1" "AMPD2" "MVK" "PRDX1" "LRP1" "AP1B1" "PAK2" "PTK7" "EIF4A2" "GANAB" "PTPA" "RABEP1" "NEDD8"
[66] "DBN1" "FSCN1" "TMEM35A" "SLC25A24" "CACNA2D3" "AFAP1" "GRPEL2" "IRGQ" "ARPC1A" "AP2M1" "PARK7" "CHID1" "FSD1L"
[79] "TTYH3" "FXYD6" "CPVL" "ANO10" "CHCHD3" "TMOD2" "EPS15L1" "DBNL" "STOML2" "PTBP2" "SLC39A10" "CFL2"
length(overlap_genes)
[1] 90
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_PRKN_DPE.csv")
print("saved")
[1] "saved"
# IGSF9B pink parkin
contrast_list <- c("PINK1-KO", "PRKN-KO", "IGSF9B-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B PINK1 PRKN")
[1] "Overlap of IGSF9B PINK1 PRKN"
print(overlap_genes)
[1] "TPM3" "TMEM132E" "DNAJC10" "CSRP2" "TJP1" "ZNF207" "TPD52L2" "NCK2" "PRAF2" "GLS" "FKBP9" "PGLS" "COX2"
[14] "TFRC" "CSTB" "CTSB" "CLU" "LAMP1" "CKMT1B" "CKMT1A" "COX7A2" "ARSA" "PTMS" "ATP6V1C1" "PSMA3" "CNTFR"
[27] "L1CAM" "CTNNA1" "NES" "ANXA11" "RAP1GDS1" "GTF2A1" "RAD23B" "TPI1" "RPS18" "PSME1" "PPP3CA" "SEPTIN2" "PAFAH1B3"
[40] "MAPRE2" "EFNB3" "H2BC21" "ATAT1" "CEP170" "STT3B" "KHSRP" "ERGIC1" "DYNLL2" "PANX1" "PFDN5" "TPPP3" "NAPB"
[53] "C17orf75" "PLCB1" "P4HTM" "PODXL2" "TMEM63C" "CDV3" "RTCB"
length(overlap_genes)
[1] 59
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_PINK1_PRKN_DPE.csv")
print("saved")
[1] "saved"
# PRKN IGSF9B
contrast_list <- c("IGSF9B-KO", "PINK1-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B PINK1")
[1] "Overlap of IGSF9B PINK1"
print(overlap_genes)
[1] "LIN7A" "DCLK1" "RNMT" "DYNC1LI2" "TGOLN2" "SPAG9" "SYNCRIP" "IDH1" "AP2A2" "GPI" "SNRPA" "H1-4"
[13] "DES" "DDX6" "MAP4" "GRN" "CORO1A" "YWHAB" "SDC2" "HSPA4" "GPX4" "ATP6V1A" "RPL35" "PAFAH1B1"
[25] "LMAN1" "HMGA2" "TUBA4A" "CLTC" "DR1" "EEF1A2" "MFGE8" "AP3B2" "MOGS" "SHH" "ATP2B3" "LSM12"
[37] "XKR4" "MIA3" "TUBA1A" "PRUNE1" "ERC1" "GATD1" "LEMD2" "NBEA" "CRELD1" "VPS35" "ERP44" "FSD1"
[49] "TRIM2" "RAI14" "MACROH2A2" "SEPTIN3" "NRBP1" "DNM3"
length(overlap_genes)
[1] 54
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_PINK1_DPE.csv")
print("saved")
[1] "saved"
# INPP5F IGSF9B
contrast_list <- c("IGSF9B-KO", "INPP5F-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B INPP5F")
[1] "Overlap of IGSF9B INPP5F"
print(overlap_genes)
[1] "SYT5" "AGRN" "ASTN1" "GPAA1" "ECI2" "ADGRL2" "BCHE" "ITGA5" "LAMC1" "TOP1" "GJA1" "M6PR" "COL5A1"
[14] "MDK" "IGFBP5" "MCM3" "CCN2" "CLCN6" "H4C8" "H4C9" "H4C6" "H4C12" "H4C5" "H4C13" "H4C1" "H4C4"
[27] "H4C2" "H4C16" "SPTBN1" "BAX" "ASPH" "SYPL1" "EMC4" "TOR1AIP1" "ATRAID" "RAPH1" "H3C14" "H3C15" "H3C13"
[40] "HS2ST1" "H2AC21" "SRRM1" "TOMM5" "NUP210" "BRI3BP" "SEMA4D" "TSNAX" "GDF15" "RAB5IF" "FUT8" "MTCH1" "CNTNAP2"
[53] "NDRG4" "PPM1H" "THSD7A" "UCHL5" "GPC6" "CLIC4"
length(overlap_genes)
[1] 58
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_INPP5F_DPE.csv")
print("saved")
[1] "saved"
# INPP5F SH3GL2
contrast_list <- c("SH3GL2-KO", "INPP5F-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SH3GL2 INPP5F")
[1] "Overlap of SH3GL2 INPP5F"
print(overlap_genes)
[1] "RAB27B" "SCD" "MID1" "MYO1B" "NOL3" "EML2" "AGR2" "CST3" "S100A6" "ABCB1" "MTHFD2" "ANK1" "MSN"
[14] "CLIP1" "SERPINB6" "CAP2" "TMPO" "NPTX2" "P51784" "MAP2K1" "GFPT1" "SRSF5" "ITIH4" "OPCML" "DLG2" "PTGIS"
[27] "PCK2" "ALDH1L2" "MDGA2" "AHNAK2" "Q8IXS6" "CMIP" "FAM177A1" "GPC2" "GPT2" "SNRNP27" "ARHGEF2" "WBP2" "SPOCK3"
[40] "FAM107B" "CYSTM1" "ALG2" "POMK" "DCTPP1" "ZCCHC3" "ARMCX1" "XPR1" "DNAJB4" "PCDHA4" "FNDC3A" "HYOU1"
length(overlap_genes)
[1] 51
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SH3GL2_INPP5F_DPE.csv")
print("saved")
[1] "saved"
# SNCA A53T pink parkin
contrast_list <- c("PINK1-KO", "PRKN-KO", "SNCA-A53T")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SNCA A53T PINK1 PRKN")
[1] "Overlap of SNCA A53T PINK1 PRKN"
print(overlap_genes)
[1] "MBNL1" "PON2" "PGM3" "GDAP1" "NONO" "SLC1A3" "PALM2AKAP2" "SEC31A" "TNPO3" "E9PJP2" "RPL28"
[12] "BET1" "RPS5" "RPL21" "RPS16" "GOLIM4" "CRTAP" "SLC25A12" "CALB1" "RPSA" "RPS2" "RPL35A"
[23] "SCP2" "RPS3" "AHCY" "ITGB8" "RPL10" "GGCX" "NNMT" "RPS10" "GNAQ" "COPB1" "COPA"
[34] "ATP1B3" "GNG2" "EIF4A1" "RPS20" "SNAP25" "RPS7" "RPS4X" "RPL30" "GNAI1" "RACK1" "EEF1A1"
[45] "CNTNAP1" "RPL24" "FKBP3" "DHX9" "SND1" "HNRNPLL" "RBMXL1" "MRPL9" "VAT1L" "CMTM6" "ATP5IF1"
[56] "PROCR" "GMPPB"
length(overlap_genes)
[1] 57
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SNCAA53T_PINK1_PRKN_DPE.csv")
print("saved")
[1] "saved"
# bright genome and IGSF9B
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SNCA GBA PINK1 PRKN IGSF9B")
[1] "Overlap of SNCA GBA PINK1 PRKN IGSF9B"
print(overlap_genes)
[1] "NCAM1" "STXBP1" "PALM3" "P4HB" "CYCS" "CTSD" "SNCA" "NEFM" "TP53I11" "DCX" "CLN5" "SNCG" "HRAS"
[14] "NEFL" "ANXA2" "SYP" "ANXA5" "GNAO1" "HSPA5" "RALA" "ATP1A3" "P4HA1" "GNS" "PPP3CB" "SYN1" "GAP43"
[27] "EPHB2" "ACAA2" "HDGF" "RPS13" "GNB1" "SET" "PLOD1" "LGALS3BP" "GALNT2" "CNTN1" "DNAJC3" "STX1A" "GDAP1L1"
[40] "CEND1" "CADM2" "GLG1" "CMBL" "DIRAS2" "FUCA2" "TMOD3" "PLXNA1"
length(overlap_genes)
[1] 47
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SNCAA53T_GBA_PINK1_PRKN_IGSF9B_DPE.csv")
print("saved")
[1] "saved"
# bright genome
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SNCA GBA PINK1 PRKN")
[1] "Overlap of SNCA GBA PINK1 PRKN"
print(overlap_genes)
[1] "HDLBP" "A0A0J9YYL3" "A0A1P0AYU5" "RPS14" "HSPA12A" "OBSCN" "SNRPE" "SCFD1" "RPS3A" "F8W6I7" "CD44"
[12] "H3BQZ7" "CBX1" "HNRNPUL1" "KPNA6" "SSB" "PYGL" "DCN" "NCL" "HNRNPA2B1" "ATP2B4" "FKBP2"
[23] "EEF1D" "PRDX6" "RPS19" "SRP9" "RPS8" "RPS23" "RPL23A" "RPL10A" "ABAT" "ERH" "NUCB1"
[34] "CRYZ" "AHNAK" "TRIM28" "FNDC3B" "GOLPH3" "CTPS2" "SEC23IP"
length(overlap_genes)
[1] 40
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SNCAA53T_GBA_PINK1_PRKN_DPE.csv")
print("saved")
[1] "saved"
# bright genome
contrast_list <- c("SH3GL2-KO", "INPP5F-KO","IGSF9B-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SH3GL2 INPP5F IGSF9B")
[1] "Overlap of SH3GL2 INPP5F IGSF9B"
print(overlap_genes)
[1] "APBB1" "PCDH7" "SLIT2" "ATP6V1G2" "PCSK1" "DCC" "EPHA4" "PLTP" "GFRA1" "ANK2" "APBA1" "PCDH1" "CAMK2G"
[14] "PLCB4" "DECR1" "BRINP3" "NEGR1" "ACOT1" "CNTN4" "NLGN4X" "IGSF1" "DLG3" "CCDC51" "SERPINI1" "EDEM3" "CHODL"
[27] "CRTAC1" "NLGN3" "MYOF" "SEMA5B" "EPHA6"
length(overlap_genes)
[1] 31
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_INPP5F_SH3GL2_DPE.csv")
print("saved")
[1] "saved"
# IGSF9B pink parkin SNCA
contrast_list <- c("PINK1-KO", "PRKN-KO", "IGSF9B-KO","SNCA-A53T")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B PINK1 PRKN SNCA-A53T")
[1] "Overlap of IGSF9B PINK1 PRKN SNCA-A53T"
print(overlap_genes)
[1] "TMED7-TICAM2" "SCARB2" "RPS24" "UGP2" "PTPRD" "NPC2" "PTMA" "FXYD7" "RPN1" "GAA"
[11] "G6PD" "PCNA" "GLB1" "ANXA7" "STT3A" "TMED10" "SSR4" "BASP1" "LMAN2" "INA"
[21] "P3H1" "HEXB" "NT5DC3" "FKBP10" "MBOAT7" "NUCKS1" "ASAP1" "TLN1" "FKBP7"
length(overlap_genes)
[1] 29
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SNCAA53T_IGSF9B_PINK1_PRKN_DPE.csv")
print("saved")
[1] "saved"
contrast_list <- c("PINK1-KO", "PRKN-KO", "IGSF9B-KO","GBA-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B PINK1 PRKN GBA")
[1] "Overlap of IGSF9B PINK1 PRKN GBA"
print(overlap_genes)
[1] "VTA1" "LMNA" "PRCP" "MAN2B1" "GLRX3" "OAT" "NAGA" "VDAC1" "HMGCS1" "PDAP1" "FLNC" "CELF1" "RAB3C"
[14] "NAPG" "MAP1LC3A" "SLC25A13" "SAMM50"
length(overlap_genes)
[1] 17
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/GBA_IGSF9B_PINK1_PRKN_DPE.csv")
print("saved")
[1] "saved"
# A53T IGSF9B
contrast_list <- c("IGSF9B-KO", "SNCA-A53T")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B SNCA A53T")
[1] "Overlap of IGSF9B SNCA A53T"
print(overlap_genes)
[1] "SDCBP" "DNAJC6" "PALM" "PAK3" "CLSTN1" "ACYP1" "UCHL1" "MAPT" "STMN1" "CANX" "PRKAR2B" "PFN2" "PPP3CC"
[14] "MARCKSL1" "RAC1" "TUBB2A" "CRMP1" "DPYSL3" "GNAS" "SLC27A4" "RHOT2" "CHMP6" "ZC2HC1A" "TMX3" "TM9SF3" "GGT7"
[27] "NRXN1"
length(overlap_genes)
[1] 27
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_SNCAA53T_DPE.csv")
print("saved")
[1] "saved"
# A53T IGSF9B
contrast_list <- c("IGSF9B-KO", "SH3GL2-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B SH3GL2")
[1] "Overlap of IGSF9B SH3GL2"
print(overlap_genes)
[1] "KPNA4" "IDH3B" "STAMBP" "CD63" "SRC" "HMGA1" "STOM" "MARCKS" "TSPAN7" "PCBP3" "IGBP1" "TGFBI" "TSC22D1" "MAVS" "ADCK1"
[16] "SCAMP5" "CD99L2" "PDXP" "SH3GL2" "SEZ6L" "ACTR3B" "EPDR1" "SLC8A2" "TLN2" "NUMBL"
length(overlap_genes)
[1] 25
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_SH3GL2_DPE.csv")
print("saved")
[1] "saved"
# A53T IGSF9B
contrast_list <- c("IGSF9B-KO", "IP6K2-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IGSF9B IP6K")
[1] "Overlap of IGSF9B IP6K"
print(overlap_genes)
[1] "SNX4" "PENK" "SERPINE2" "NGFR" "MGST1" "PTN" "IDH3A" "SYNJ2BP" "SARNP" "TUSC3" "ANO6" "TCEAL5" "EARS2"
[14] "SNX30" "ATCAY" "THNSL1" "SHC3" "PPP1R10" "VAT1" "TM9SF2" "PACSIN1" "SLC5A7" "TBC1D24" "CAMK2A" "PPP2R2C"
length(overlap_genes)
[1] 25
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_IP6K2_DPE.csv")
print("saved")
[1] "saved"
# A53T PINK1
contrast_list <- c("PINK1-KO", "SNCA-A53T")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of PINK1KO SNCA A53T")
[1] "Overlap of PINK1KO SNCA A53T"
print(overlap_genes)
[1] "TARDBP" "DDX17" "NPM1" "RPL8" "SEC22B" "RPLP0" "RAB3A" "SFPQ" "RPL22" "RPS9" "HNRNPM" "HNRNPK" "RPL7A" "TOP2B" "DNM1" "GOLGB1"
[17] "RAB35" "SF3B3" "Q5T0I0" "PLPPR3" "MB21D2" "CMAS" "BRSK1" "MAP6"
length(overlap_genes)
[1] 24
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PINK1_SNCAA53T_DPE.csv")
print("saved")
[1] "saved"
# A53T PINK1 GBA
contrast_list <- c("PINK1-KO", "SNCA-A53T","GBA-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of PINK1KO SNCA A53T and GBA")
[1] "Overlap of PINK1KO SNCA A53T and GBA"
print(overlap_genes)
[1] "HNRNPU" "RPL5" "HNRNPC" "B4E171" "HNRNPH1" "HMGN1" "RPL17" "DDX5" "HSPA4L" "CA2" "XRCC6" "SNRPB" "LMNB1"
[14] "RPL23" "NCBP1" "ATP6V0A1" "THRAP3"
length(overlap_genes)
[1] 17
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PINK1_SNCAA53T_GBA_DPE.csv")
print("saved")
[1] "saved"
# A53T PINK1 IGSF9B
contrast_list <- c("PINK1-KO", "SNCA-A53T","IGSF9B-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of PINK1KO SNCA A53T and IGSF9B")
[1] "Overlap of PINK1KO SNCA A53T and IGSF9B"
print(overlap_genes)
[1] "SHTN1" "SNAP91" "TUBB4A" "NSF" "ACADVL" "RPL26" "RHOB" "CXADR" "CKAP4" "OS9" "TUBB3" "MVP" "TMED2" "PRRT2" "NAT10" "WDR37"
length(overlap_genes)
[1] 16
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PINK1_SNCAA53T_IGSF9B_DPE.csv")
print("saved")
[1] "saved"
# dark genome
contrast_list <- c("SH3GL2-KO", "INPP5F-KO","IGSF9B-KO", "IP6K2-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of SH3GL2 INPP5F IGSF9B")
[1] "Overlap of SH3GL2 INPP5F IGSF9B"
print(overlap_genes)
[1] "PCP4L1" "SYNM" "CSPG5" "UTS2" "VAMP1" "NOVA1" "DYNLT3" "CARTPT" "PDK4" "LBH" "FAM162A" "SLC6A17" "SLC17A6" "SUN2"
length(overlap_genes)
[1] 14
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_INPP5F_SH3GL2_IP6K2_DPE.csv")
print("saved")
[1] "saved"
Individual contrasts
contrast_list <- c("SNCA-A53T")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to SNCA A53T")
[1] "Unique to SNCA A53T"
print(overlap_genes)
[1] "ABHD6" "NUDT9" "AGAP1" "GUCY1B1" "GOSR1" "COPZ1" "DDX3Y" "NRAS" "QDPR" "ARF4" "TAGLN2" "GNG4" "EIF5"
[14] "MRPS5" "SF3B2" "XKR7" "RPL11" "ASRGL1" "PLD3" "FAM114A1" "HYCC2" "HSD17B11" "TTN" "LRRC59" "FAM241B" "SDF2"
[27] "CADM1" "UBE2O" "SAR1A" "BPNT2" "BSN" "ATP8A1" "NPTN"
length(overlap_genes)
[1] 33
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SNCAA53T_DPE.csv")
print("saved")
[1] "saved"
contrast_list <- c("GBA-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to GBA KO")
[1] "Unique to GBA KO"
print(overlap_genes)
[1] "MADD" "IDH3G" "PLXNC1" "NNT" "RHEB" "HSDL1" "ABCF1" "RAPGEF4" "GDA"
length(overlap_genes)
[1] 9
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/GBA_DPE.csv")
print("saved")
[1] "saved"
contrast_list <- c("PINK1-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to PINK1 KO")
[1] "Unique to PINK1 KO"
print(overlap_genes)
[1] "A0A087WTM1" "A0A087WY61" "CD99" "MYEF2" "A0A0A0MR09" "ILK" "ENAH" "QARS1" "DLG1" "TCOF1" "UBE3A"
[12] "PPIG" "RMND1" "RABGAP1L" "IARS1" "PTBP1" "ILF2" "PREB" "PLRG1" "HNRNPAB" "SKP1" "ALYREF"
[23] "CELF2" "ADCYAP1R1" "HSPA8" "LAMTOR1" "UBAP2L" "F8WE88" "ARFGAP2" "RPL36A" "TMEM199" "SRSF1" "FARSA"
[34] "RPL18A" "HIP1" "HMGN4" "HNRNPR" "CHMP2A" "AHCYL1" "PLIN3" "SF3B1" "CPD" "SEC24D" "UFL1"
[45] "ABCA8" "KRAS" "FGF1" "EPRS1" "HSP90AA1" "PFKM" "SNRPB2" "PYGB" "DARS1" "IGFBP4" "RPL12"
[56] "HNRNPH3" "FUS" "ATP6V1E1" "USP8" "LSS" "HNRNPA3" "MARS1" "EIF6" "NUTF2" "PPP1CB" "RPS11"
[67] "SNRPG" "SNRPD1" "SNRPD3" "ACTA2" "RPL31" "ACY1" "LMNB2" "KHDRBS1" "SF3A3" "ILF3" "CBX3"
[78] "CUL3" "SEPTIN7" "H2AC20" "SF3B4" "MRPL14" "PRPF8" "PPP1R21" "DGLUCY" "CENPV" "CCAR2" "GPD1L"
[89] "NUP43" "CTNNBL1" "HDAC2" "SLC25A46" "FAF2" "VMP1" "API5" "EHD4" "RPAP3" "FARSB" "SLTM"
[100] "HDAC6" "CADPS" "LSM2" "SF3B6" "DAAM1" "ATP6V1D"
length(overlap_genes)
[1] 105
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PINK1_DPE.csv")
print("saved")
[1] "saved"
contrast_list <- c("PRKN-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to PRKN KO")
[1] "Unique to PRKN KO"
print(overlap_genes)
[1] "HMOX2" "A0A087WTF6" "A0A087WUC6" "MTHFD1L" "PSME2" "PSMC6" "ELAVL2" "ATP6AP1" "HSPA1B" "HSPA1A" "KAZN"
[12] "BRAF" "CTNNB1" "SUCLA2" "RAPGEF2" "PDE10A" "ABCD3" "CLUH" "IMPDH2" "NDUFA10" "ADSL" "NAXD"
[23] "CUL4B" "AASS" "DLAT" "SH3PXD2B" "ACO2" "ENSA" "SEC13" "MYL6" "IMMT" "DTNBP1" "EIF4E"
[34] "COPS6" "NDUFV2" "WASHC5" "PLOD2" "PAICS" "NPEPPS" "ABLIM2" "PXN" "MTHFD1" "SEC23A" "MYG1"
[45] "LIMA1" "ARL1" "F8W809" "GPATCH4" "LIMS1" "G3V180" "APOO" "PIN4" "SQLE" "H3BNC9" "LMF1"
[56] "ACTN4" "TTC27" "NAA38" "TUBB6" "SARS2" "AP3B1" "PDHX" "PSMD14" "PPP6C" "TNPO2" "SCAMP3"
[67] "ERC2" "ARPC3" "CAPN5" "NDUFA2" "NDUFS5" "TIMM8A" "NRP2" "OGA" "RANBP6" "ROCK2" "CSDE1"
[78] "STAM2" "ATP5PD" "PLPBP" "LYPLA2" "IPO7" "ECEL1" "AIFM1" "SOD1" "PGK1" "GAPDH" "ATP1A1"
[89] "ALDH2" "ATP5F1B" "ENO1" "PFN1" "HSP90AB1" "ACAA1" "DLD" "ESD" "SLC2A3" "PC" "SLC25A6"
[100] "CKB" "RNH1" "TPT1" "ETFA" "AKR1A1" "GSPT1" "NCK1" "PSMC3" "VCL" "CALB2" "ACP1"
[111] "TARS1" "MAPK1" "ALDH4A1" "PRDX3" "ATP5F1D" "ADSS2" "SLC7A1" "HIBADH" "ATIC" "KIF5B" "AGL"
[122] "PSMC2" "PGM1" "GNL1" "ETFB" "RBMX" "HSPA9" "ADCY8" "EIF2S3" "USP5" "GNPDA1" "ATP5PO"
[133] "PREP" "IDH2" "TUFM" "AARS1" "HINT1" "EMD" "CCT4" "RAB7A" "ALDH3A2" "CAPZA1" "CRIP2"
[144] "SLC25A1" "ARFIP1" "SUCLG1" "PRKAG1" "CSE1L" "VCP" "ATP5MJ" "EEFSEC" "ARPC4" "DSTN" "RAB8A"
[155] "UBE2M" "SST" "RAN" "AP2B1" "PPP2R2A" "MRPS15" "TFAM" "GLO1" "AKR1C1" "SSBP1" "PTPN11"
[166] "APLP2" "C1QBP" "VAC14" "AIMP1" "TRAP1" "TRAF2" "PRKAA1" "MRPL49" "SLC39A6" "PICALM" "NAE1"
[177] "CTTN" "HES1" "KPNB1" "GAPVD1" "RAB3GAP1" "PEA15" "TAB1" "CDC37" "NDUFA5" "HADH" "LRRFIP1"
[188] "MTUS2" "ACADM" "SARS1" "NT5DC1" "ATG9B" "HIBCH" "PKN3" "SARM1" "VPS13C" "FASTKD5" "HUWE1"
[199] "STX12" "FUNDC1" "SULF1" "TPH2" "GSPT2" "LGI2" "NDUFAF2" "LRRC47" "ARMC10" "RDH11" "LARP4B"
[210] "GCN1" "MRPS31" "NECTIN2" "TFG" "STAM" "GCDH" "IGSF8" "PHYHIPL" "IPO9" "PSMB7" "APOL2"
[221] "CORO1B" "CNPY3" "MRPS26" "LNPK" "RTN4" "NANS" "OLA1" "ABHD10" "ETNK2" "CISD1" "LMCD1"
[232] "PTGFRN" "ANKFY1" "ATXN10" "COPG2" "STK39" "TRMT112" "VPS28" "DNAJC12" "ZMYND8" "DDX19B" "MAGED2"
[243] "PSMD13" "RUVBL2" "PLAA" "RUVBL1" "MRPS28" "MRPS17" "TMA7" "ACOT9" "LUC7L2" "STRAP" "PPME1"
[254] "CDC42BPB" "HEBP2" "PSAT1"
length(overlap_genes)
[1] 256
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/PRKN_DPE.csv")
print("saved")
[1] "saved"
contrast_list <- c("IGSF9B-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to IGSF9B")
[1] "Unique to IGSF9B"
print(overlap_genes)
[1] "ESYT2" "ILVBL" "DENND3" "CASTOR2" "FAM171A2" "MCRIP1"
[7] "SLC35A4" "DNASE2" "KIF2A" "MYO1C" "DFFA" "MANBA"
[13] "RNASET2" "PODXL" "ARID1A" "SDHD" "COX7A2L" "UBFD1"
[19] "ABLIM1" "ADAM10" "EI24" "PDCD5" "TPP1" "CPLX1"
[25] "PSMA7" "TIMM23" "UQCRQ" "CLGN" "SPTBN2" "KIF3B"
[31] "SNPH" "DEGS1" "SCAMP2" "PGRMC2" "PFDN6" "RER1"
[37] "SPTLC1" "HMGB3" "STX7" "YKT6" "FABP7" "SEPTIN4"
[43] "SRGAP3" "TXNL1" "SYNJ1" "PROM1" "TSPAN6" "NDUFB5"
[49] "NDUFB3" "AP1G1" "SGTA" "SLC25A20" "NUDT21" "CALU"
[55] "EXTL3" "MFSD11" "KIF5C" "SELENOF" "TSPAN2" "TSPAN3"
[61] "DPM1" "TIMM17B" "CUTA" "PFDN1" "ABCB7" "SRGAP2"
[67] "ATP9A" "CLASP2" "PPFIA3" "TBCA" "MPDU1" "MACROH2A1"
[73] "PEX14" "NDUFB1" "ERLIN1" "HSBP1" "BANF1" "TIPRL"
[79] "PPM1B" "ADAP1" "ATP6AP2" "EIF3G" "EIF3J" "ATRN"
[85] "DGAT1" "ARL6IP5" "FLOT1" "TRIO" "RSL1D1" "B3GAT3"
[91] "DDHD2" "NFASC" "TMCC1" "ENDOD1" "WDR47" "YIF1A"
[97] "NDUFB4" "SBF1" "VAPB" "SNAPIN" "NDUFC2" "OXSR1"
[103] "GGPS1" "BAG2" "NDUFB10" "TOMM40" "PEX11B" "HPRT1"
[109] "ATP6" "IGF2" "APOE" "APOC3" "FTH1" "GBA1"
[115] "HLA-A" "RPN2" "SCG5" "ATP5MC1" "ITGB1" "GLA"
[121] "HEXA" "EPHX1" "LDHB" "PSAP" "LAMB1" "ENO2"
[127] "CLTA" "ANXA4" "COX6C" "SMIM13" "RAP2A" "TXN"
[133] "CTSA" "CHGA" "ACP2" "MAP2" "DBT" "RALB"
[139] "TOP2A" "UBL4A" "IGF2R" "COL6A1" "LAMP2" "PRKAR2A"
[145] "MIF" "UCHL3" "CD46" "ARSB" "RPA2" "COX7C"
[151] "H1-5" "H1-3" "H1-2" "GOT1" "NDUFB7" "GM2A"
[157] "SON" "GNAZ" "RAB3B" "GSTM3" "SYT1" "OSBP"
[163] "FBL" "FDXR" "GCSH" "CFL1" "EEF1B2" "GRK2"
[169] "PSMA1" "YWHAQ" "MARK3" "PSMB5" "TMOD1" "LAP3"
[175] "IMPA1" "GDI1" "TIA1" "UQCRC1" "STIP1" "S100A11"
[181] "GALNS" "MYH10" "ADD1" "BSG" "PPM1A" "HMGCL"
[187] "P36268" "DLST" "NUP62" "LIPA" "COL18A1" "MDH1"
[193] "HADHA" "CETN2" "CD200" "ACTR1B" "EPS15" "CASP3"
[199] "ECE1" "SSR1" "MAP1B" "RAP1GAP" "CAPZB" "TFPI2"
[205] "PIP4K2A" "CD151" "HSPA13" "AMPH" "INPP1" "HARS2"
[211] "PSEN1" "TSC2" "GSK3B" "NT5C2" "SEPHS1" "CPT1A"
[217] "ST13" "HCFC1" "ALDH5A1" "PSMD7" "KIF11" "MVD"
[223] "CTSC" "PTTG1IP" "SLC16A1" "RAD23A" "ALDH18A1" "MFAP2"
[229] "AFDN" "HNRNPH2" "BID" "ARPP19" "AP1S2" "CORO7"
[235] "EPPK1" "SEC61B" "S100A10" "UBE2N" "RPL27" "PCBD1"
[241] "SEC61A1" "DAD1" "PKIA" "SUMO2" "UFM1" "AP1S1"
[247] "YWHAG" "YWHAE" "ARF6" "RPS6" "RBX1" "RPL32"
[253] "PPIA" "VAMP2" "YWHAZ" "PPP2CA" "SEC11A" "UBE2L3"
[259] "TUBB4B" "GTF2I" "PIP4K2B" "SLC35A1" "ARG2" "MRPS11"
[265] "ARF5" "H3-3A" "H3-3B" "HSPG2" "SLC25A3" "CDK5"
[271] "TIAL1" "CTBS" "CAP1" "ATP2B2" "OCRL" "SLC25A11"
[277] "PTS" "KLC1" "ARHGAP1" "GOLGA3" "DMTN" "PPID"
[283] "SCRN3" "KIF1A" "SCRN1" "ANK3" "PAK1" "NME3"
[289] "MAD2L1" "UBE2V1" "PEDS1-UBE2V1" "DYNC1I2" "TMED1" "MTX1"
[295] "CAMK2B" "DCTN2" "STIM1" "ITGA7" "SPTAN1" "HNRNPD"
[301] "DAG1" "MLEC" "TTLL12" "MPP2" "DCTN1" "FLOT2"
[307] "INPP5A" "CLINT1" "LBR" "SPCS2" "EMC2" "PSMD6"
[313] "RRS1" "POSTN" "PDIA6" "PPP2R5B" "RCN1" "SF1"
[319] "MAPRE1" "RAB30" "ITSN1" "TBCE" "UBE2V2" "VAMP3"
[325] "DPYSL2" "CPSF6" "SMN2" "SMN1" "TST" "HAGH"
[331] "PTPRO" "HNRNPUL2" "CCDC88A" "LGALSL" "VPS26B" "CCDC184"
[337] "HSD17B12" "RAB6D" "SDK2" "TMEM97" "YIF1B" "MEST"
[343] "NOMO2" "WDR44" "SAMD4B" "CLVS2" "TPRG1L" "FNBP1L"
[349] "GPR158" "WLS" "Q5TF21" "MICOS10" "WASHC2A" "PPP2R2D"
[355] "MBLAC2" "LMBRD2" "IQSEC1" "CARMIL2" "RALGAPA1" "TCEAL6"
[361] "REEP3" "ARMC6" "JMJD6" "PPP1R18" "SLC48A1" "EDC4"
[367] "SCYL2" "CNNM4" "ERICH5" "MEAK7" "PGM2L1" "AAGAB"
[373] "TMEM65" "NCEH1" "CPLX2" "MOXD1" "CYP20A1" "POGLUT2"
[379] "TMEM205" "ISLR2" "APOOL" "PACS1" "RAB11FIP1" "HSDL2"
[385] "CRACD" "GPRIN3" "TOM1L2" "TMTC3" "IKBIP" "UBE2R2"
[391] "TLCD3B" "RUFY3" "EPM2AIP1" "TAOK1" "MOB1B" "CHMP1B"
[397] "MICAL3" "NUP54" "KIF21A" "TMED4" "GALNT7" "MAGI2"
[403] "USP48" "CAND1" "OSTM1" "DDX42" "NDUFA11" "DOLPP1"
[409] "GPSM1" "NUDCD3" "SLC44A2" "BRSK2" "SULF2" "SIRT2"
[415] "NRM" "SMAP1" "KIAA0319L" "NUP93" "ABHD12" "SLC43A2"
[421] "MMGT1" "CALHM5" "KCNRG" "JAGN1" "COMMD1" "EMC1"
[427] "AMER2" "GOLM1" "COLGALT1" "SUMF2" "TMEM87A" "TXNDC5"
[433] "NECAP1" "CAMKV" "PLA2G15" "Q8NCU8" "MROH1" "EHBP1"
[439] "MCU" "NUP37" "NLGN2" "CADM4" "FGFBP3" "UBA3"
[445] "C18orf32" "GPX8" "RAPGEF6" "C16orf78" "PDCD6IP" "PSPC1"
[451] "PPM1E" "JDP2" "LZIC" "DDX1" "H1-10" "NCSTN"
[457] "TM9SF4" "WASF1" "SLC9A6" "NDRG1" "NUP205" "TTC9"
[463] "SORL1" "GGH" "NRCAM" "KIFAP3" "PTPRN2" "USP9X"
[469] "BORCS5" "UBE2E3" "NCLN" "CCDC47" "TMEM230" "FUBP1"
[475] "VTI1A" "MCUR1" "TOMM6" "ARL8A" "CCDC127" "EFHD2"
[481] "PYCR2" "DCPS" "PPP1R14B" "ISOC1" "FOXRED1" "RAB39B"
[487] "ERLEC1" "DAZAP1" "SGTB" "CYFIP2" "CNRIP1" "LINGO1"
[493] "OTUB1" "CERS2" "SFRP2" "CDK5RAP3" "FAM210B" "PHACTR3"
[499] "PRRC1" "VSTM2L" "RUNDC3B" "NAP1L5" "AGAP3" "MIA2"
[505] "NEDD4L" "MAGI1" "PIGS" "YME1L1" "TBCB" "SEC62"
[511] "TSC22D3" "TTC1" "PHB2" "ATXN2" "SIGMAR1" "EBNA1BP2"
[517] "H2BC13" "ECSIT" "SYT3" "TXNDC17" "NUDT16L1" "SDF4"
[523] "ESYT1" "TMEM43" "LMF2" "TUBB2B" "TMEM109" "PBDC1"
[529] "PTDSS2" "TMED9" "ACAT2" "AP1M1" "RAB34" "SEMA4C"
[535] "YIPF3" "NDEL1" "ROGDI" "PITHD1" "MAP1LC3B" "UBA5"
[541] "KLC2" "HDHD2" "MAGT1" "LMAN2L" "SIL1" "SH3BGRL3"
[547] "SLC38A1" "DPAGT1" "POFUT1" "PIGU" "CLSTN2" "EPB41L1"
[553] "SMDT1" "PRR36" "REEP1" "SFXN1" "COG4" "NMNAT1"
[559] "TMEM165" "GLOD4" "PCDH9" "MCCC2" "APMAP" "NPHS2"
[565] "DYNLRB1" "EMC7" "PFDN4" "EIF2B3" "RAB6B" "TOMM22"
[571] "LRRC4B" "RBM12" "STAU2" "SHFL" "TMEM106B" "AGPAT5"
[577] "CYRIB" "EXOC1" "TMEM38B" "NDUFB11" "OCIAD1" "BABAM2"
[583] "DPP3" "SCN3A" "SCN3B" "TERF2IP" "UGGT1" "ERAP1"
[589] "CHMP5" "HACD3" "KCMF1" "TBC1D7" "TBC1D7-LOC100130357" "TOMM7"
[595] "CAMSAP3" "DPM3" "NCDN" "MRC2" "CPNE7" "EXTL2"
[601] "DNAJB11" "SEL1L" "PEF1" "ZMYM2" "SLC25A10" "CLIP2"
[607] "CFDP1" "SEC63" "CHORDC1" "UBQLN2" "PCYOX1" "PFDN2"
[613] "PUF60" "NAGK" "SH3BGRL2" "PURG" "GGA1" "JPT1"
[619] "TNIK" "PITPNC1" "MAN1B1" "CORO1C" "HPCAL4" "MYO6"
[625] "UBQLN1" "SYNRG" "SSR3" "FAF1" "MINPP1" "NOVA2"
[631] "NSFL1C" "MAPK8IP3" "USP24" "AGTPBP1" "MAPRE3" "CORO2B"
[637] "NUDC" "MAN2B2" "EFR3B" "DIS3" "GSTK1" "LAMTOR2"
[643] "CARHSP1" "SUGT1" "COQ6" "NSG2" "SH3GLB1" "DHRS7"
[649] "TMED5" "TMED7" "HDGFL3" "RABGAP1" "TMED3" "WNK2"
[655] "KIF3A" "USP15" "MYO5A" "RBM7" "TIMM22" "TIMM10B"
[661] "IER3IP1" "LRRFIP2" "SPIN1" "SPCS1" "NDUFB9" "FAM169A"
length(overlap_genes)
[1] 666
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IGSF9B_DPE.csv")
print("saved")
[1] "saved"
contrast_list <- c("INPP5F-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to INPP5F")
[1] "Unique to INPP5F"
print(overlap_genes)
[1] "PIGBOS1" "CLIC1" "STX16" "U2SURP" "NACAD" "XPOT" "STX6" "SIN3B" "DAB1" "TUSC2" "ACTL6B" "ZRANB2" "BAG3" "CFB" "ASS1"
[16] "P01861" "APOH" "HPX" "S100B" "SNRNP70" "SLC2A1" "POLR2E" "AK4" "ERP29" "GCHFR" "SHMT2" "GARS1" "NSG1" "SLC1A4" "SUB1"
[31] "MANF" "TRA2B" "SRSF2" "PPARD" "FLII" "SRSF6" "SQSTM1" "PRP4K" "IDI1" "SAFB2" "SMC1A" "CHD4" "RNPS1" "TERF2" "PTPRN"
[46] "UBR4" "ARFGEF3" "ARSK" "ARMCX2" "NDUFAF7" "MICALL1" "PRUNE2" "COPS9" "KCTD12" "PPP1R9B" "CNN2" "SF3B5" "C1QTNF4" "SPRY4" "PNN"
[61] "GHITM" "EHMT1" "GMPR2" "TAGLN3"
length(overlap_genes)
[1] 64
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/INPP5F_DPE.csv")
print("saved")
[1] "saved"
contrast_list <- c("SH3GL2-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Unique to SH3GL2")
[1] "Unique to SH3GL2"
print(overlap_genes)
[1] "AIP" "GTPBP1" "MGRN1" "CNTN5" "KBTBD11" "POLR2C" "GLRX" "ALDH7A1" "KPNA1" "TPD52" "NCALD" "GRM7" "ETFDH"
[14] "FRY" "TBC1D9B" "ZC3HAV1" "ARHGAP12" "GDPD1" "NUDT10" "ITFG1" "MICAL1" "SH3KBP1" "CHAMP1" "LYSMD1" "TSPAN18" "OLFM1"
[27] "DNAJC5" "HEBP1" "ABCB8" "TMEM30A" "GDE1" "GPRC5B" "EHD3" "CACFD1" "SEPTIN9" "NFU1" "GAB2"
length(overlap_genes)
[1] 37
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/SH3GL2_DPE.csv")
print("saved")
[1] "saved"
contrast_list <- c("IP6K2-KO")
overlap_genes <- get_specific_gene_overlap(filtered_DEP, contrast_list)
# Print the result
print("Overlap of IP6K2")
[1] "Overlap of IP6K2"
print(overlap_genes)
[1] "BRD4" "PRKCA" "PLCG1" "FDFT1" "MTIF2" "DLG4" "EPS8" "RAPGEF1" "FKBP8" "CYP51A1" "PRPF40B" "SCAI" "SYNE1" "FBXO41" "SPOCK2"
[16] "EVI5L" "FYTTD1" "CAMK2N2" "NT5DC2" "SERINC1" "RPRM" "TES" "OGDHL" "TDRKH" "TMX2"
length(overlap_genes)
[1] 25
write.csv(overlap_genes,"/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/OverlapLGeneLists/IP6K2_DPE.csv")
print("saved")
[1] "saved"
Make one dataframe for expression lists
# look at each abundance dataframe
colnames(df.bright)
[1] "Symbol" "A53T.1" "A53T.2" "A53T.3" "Control.1" "Control.2" "Control.3" "GBA.KO.1" "GBA.KO.2" "GBA.KO.3" "PINK1.KO.1"
[12] "PINK1.KO.2" "PINK1.KO.3" "PRKN.KO.1" "PRKN.KO.2" "PRKN.KO.3"
colnames(df.dark)
[1] "Symbol" "Control.1" "Control.2" "Control.3" "Control.4" "IGSF9B.KO.1" "IGSF9B.KO.2" "INPP5F.KO.1" "INPP5F.KO.2" "INPP5F.KO.3"
[11] "IP6K2.KO.1" "IP6K2.KO.2" "IP6K2.KO.4" "SH3GL2.KO.1" "SH3GL2.KO.2" "SH3GL2.KO.3"
Try some plots
df.long <- protein_zscore(data = df,
proteins = lysome$`Gene name`, # Example protein names
sample_patterns = c("Control","A53T","GBA.KO","PINK1.KO","PRKN.KO","INPP5F.KO","IGSF9B.KO" ,"SH3GL2.KO","IP6K2.KO"), group_means = TRUE)
[1] "Sample columns selected: A53T.1, A53T.2, A53T.3, Control.1.x, Control.2.x, Control.3.x, GBA.KO.1, GBA.KO.2, GBA.KO.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3, PRKN.KO.1, PRKN.KO.2, PRKN.KO.3, Control.1.y, Control.2.y, Control.3.y, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, INPP5F.KO.1, INPP5F.KO.2, INPP5F.KO.3, IP6K2.KO.1, IP6K2.KO.2, IP6K2.KO.4, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
max(df.long$Abundance, na.rm = TRUE)
[1] 4.035141
min(df.long$Abundance, na.rm = TRUE)
[1] -1.118153
# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
data = df,
proteins = lysome$`Gene name`, # Example protein names
sample_patterns = c("Control", "A53T","GBA.KO","PINK1.KO","PRKN.KO","INPP5F.KO","IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1.5,-1,-0.5, 0, 2,4.5), # Adjust based on your data range
group_means = TRUE, # Set to FALSE if you want individual samples
cell_width = 20, # Control column width
cell_height = 10 # Control row height
)
[1] "Sample columns selected: A53T.1, A53T.2, A53T.3, Control.1.x, Control.2.x, Control.3.x, GBA.KO.1, GBA.KO.2, GBA.KO.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3, PRKN.KO.1, PRKN.KO.2, PRKN.KO.3, Control.1.y, Control.2.y, Control.3.y, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, INPP5F.KO.1, INPP5F.KO.2, INPP5F.KO.3, IP6K2.KO.1, IP6K2.KO.2, IP6K2.KO.4, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
# Example usage
heatmap_plot <- plot_clustered_protein_heatmap_zscore(
data = df,
proteins = lysome$`Gene name`, # Example protein names
sample_patterns = c("Control.", "A53T.","GBA.KO","PINK1.KO","PRKN.KO","INPP5F.KO","IGSF9B.KO", "SH3GL2.KO","IP6K2.KO"),
colors = c("snow", "lightgoldenrod1", "gold1", "darkorange1", "red2", "firebrick4"),
scale_values = c(-1,-0.5, 0, 2.5,5), # Adjust based on your data range
group_means = FALSE, # Set to FALSE if you want individual samples
cell_width = 20, # Control column width
cell_height = 10 # Control row height
)
[1] "Sample columns selected: A53T.1, A53T.2, A53T.3, Control.1.x, Control.2.x, Control.3.x, GBA.KO.1, GBA.KO.2, GBA.KO.3, PINK1.KO.1, PINK1.KO.2, PINK1.KO.3, PRKN.KO.1, PRKN.KO.2, PRKN.KO.3, Control.1.y, Control.2.y, Control.3.y, Control.4, IGSF9B.KO.1, IGSF9B.KO.2, INPP5F.KO.1, INPP5F.KO.2, INPP5F.KO.3, IP6K2.KO.1, IP6K2.KO.2, IP6K2.KO.4, SH3GL2.KO.1, SH3GL2.KO.2, SH3GL2.KO.3"
Heat map of Logfold change
Same function but controling keeping the gene list order
Plot by dendrogram
# now plotted with function below
gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1)
print(p1)
gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
p2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1)
print(p2)
gene_list <- lysome$`Gene name`
contrast_list <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p3 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1)
print(p3)
gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1)
print(p4)
NA
NA
gene_list <- lysome$`Gene name`
contrast_list1 <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
contrast_list2 <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
contrast_list3 <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
contrast_list4 <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/HM_lysomeList_Log2FC_dendrogram.pdf", width = 6, height = 8)
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list1, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Lysosome Genes")
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list1, remove_na_genes = TRUE, column_width = 1,title = "Log Fold Change Lysosome Genes")
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list3, remove_na_genes = TRUE, column_width = 1,title = "Log Fold Change Lysosome Genes")
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list4, remove_na_genes = TRUE, column_width = 1,title = "Log Fold Change Lysosome Genes")
dev.off()
null device
1
get another gene list from Roxanne’s list
length(mito.genes)
[1] 1136
gene_list <- mito.genes[1:20]
#gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 12)
Warning: Values from `log2_ratio` are not uniquely identified; output will contain list-cols.
• Use `values_fn = list` to suppress this warning.
• Use `values_fn = {summary_fun}` to summarise duplicates.
• Use the following dplyr code to identify duplicates.
{data} |>
dplyr::summarise(n = dplyr::n(), .by = c(Symbol, Contrast)) |>
dplyr::filter(n > 1L)
Error in `dplyr::mutate()`:
ℹ In argument: `across(-Symbol, as.numeric)`.
Caused by error in `across()`:
! Can't compute column `SNCA-A53T`.
Caused by error:
! 'list' object cannot be coerced to type 'double'
Backtrace:
1. global plot_logfold_change_heatmap_dendrogram(...)
4. dplyr:::mutate.data.frame(., across(-Symbol, as.numeric))
5. dplyr:::mutate_cols(.data, dplyr_quosures(...), by)
7. dplyr:::mutate_col(dots[[i]], data, mask, new_columns)
9. mask$eval_all_mutate(quo)
10. dplyr (local) eval()
New function to skip genes not found in df_list
print(mitocarta$Symbol[11:20])
[1] "COX5A" "ISCA2" "PMPCB" "UQCRFS1" "ATP5F1A" "OGDH" "PDHB" "UQCRC2" "SDHD" "MRPS35"
This error seems to mean there are duplicate enteries for the same gene
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[1:10], contrast_list,
remove_na_genes = TRUE, column_width = 1)
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[10:20], contrast_list,
remove_na_genes = TRUE, column_width = 1)
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[11:60], contrast_list,
remove_na_genes = TRUE, column_width = 0.8)
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol[1:20], contrast_list,
remove_na_genes = TRUE, column_width = 1)
Error in hclust(d, method = method) :
NA/NaN/Inf in foreign function call (arg 10)
We now need to remove problematic values
Force 0 to be the center of the scale
plot_logfold_change_heatmap_dendrogram <- function(df_list, gene_list, contrast_list,
remove_na_genes = FALSE, column_width = 0.8, title = "Log Fold Change") {
# Filter data to only include the specified genes and contrasts
filtered_data <- df_list[contrast_list] %>%
lapply(function(df) {
df %>%
dplyr::filter(Symbol %in% gene_list) %>%
dplyr::select(Symbol, log2_ratio)
}) %>%
dplyr::bind_rows(.id = "Contrast")
# Ensure only genes in gene_list that are found in the filtered data are kept
filtered_data <- filtered_data %>%
dplyr::filter(Symbol %in% unique(filtered_data$Symbol))
# Handle duplicates by summarizing (e.g., taking the mean)
filtered_data <- filtered_data %>%
dplyr::group_by(Symbol, Contrast) %>%
dplyr::summarize(log2_ratio = mean(log2_ratio, na.rm = TRUE), .groups = 'drop')
# Create a wide format matrix for heatmap plotting
data_wide <- filtered_data %>%
tidyr::pivot_wider(names_from = Contrast, values_from = log2_ratio) %>%
dplyr::filter(Symbol %in% gene_list) %>% # Ensure only genes in gene_list are kept
dplyr::mutate(dplyr::across(-Symbol, as.numeric))
# Optionally remove genes that are NA across all contrasts
if (remove_na_genes) {
data_wide <- data_wide %>%
dplyr::filter(rowSums(is.na(dplyr::select(., -Symbol))) < ncol(data_wide) - 1)
}
# Create a matrix for heatmap plotting
mat <- as.matrix(data_wide %>% dplyr::select(-Symbol))
rownames(mat) <- data_wide$Symbol
# Check for NA, NaN, or Inf values in the matrix and remove any rows or columns that contain them
mat <- mat[complete.cases(mat), ] # Remove rows with NA/NaN/Inf values
mat <- mat[, colSums(is.na(mat)) == 0] # Remove columns with NA/NaN/Inf values
# If after removing NA rows/columns the matrix becomes empty, return an informative error
if (nrow(mat) == 0 || ncol(mat) == 0) {
stop("The matrix is empty after removing rows/columns with NA/NaN/Inf values. No valid data to plot.")
}
# Determine the limits for the scale to be symmetric around zero
max_val <- max(abs(mat), na.rm = TRUE)
# Create the heatmap with dendrograms
pheatmap::pheatmap(mat,
cluster_rows = TRUE,
cluster_cols = TRUE,
scale = "none",
color = colorRampPalette(c("blue", "white", "red"))(50),
breaks = seq(-max_val, max_val, length.out = 51), # Symmetric color scale
cellwidth = column_width * 10, # Adjust column width
show_rownames = TRUE,
show_colnames = TRUE,
main = title)
}
plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list = mitocarta$Symbol, contrast_list,
remove_na_genes = TRUE, column_width = 1)
NA
NA
NA
Gene different heatmaps
gene_list <- mito.genes
#gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes")
print(p1)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
p2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes")
print(p2)
contrast_list <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p3 <-plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes")
print(p3)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes")
print(p4)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[1:100]
p1.1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 1-100")
p1.1
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[101:200]
p1.2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 101-200")
p1.2
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[201:300]
p1.3 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 201-300")
p1.3
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[301:400]
p1.4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 301-400")
p1.4
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[401:600]
p1.5 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 401-600")
p1.5
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[601:800]
p1.6 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 601-800")
p1.6
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- mito.genes[801:1136]
p1.7 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Mitocarta Genes 801-1136")
p1.7
NA
NA
NA
Save mitocharta plots
pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/HM_Mitocharta_Log2FC_dendrogram.pdf")
p1
p1.1
p1.2
p1.3
p1.4
p1.5
p1.6
p1.7
p2
p3
p4
dev.off()
null device
1
Read in another list of genes
head(pd.genes.list)
pd.genes <- pd.genes.list$Symbol
Look at the gene list for PD
gene_list <- pd.genes
#gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes")
print(p1)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
p2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes")
print(p2)
contrast_list <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p3 <-plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes")
print(p3)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes")
print(p4)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[1:50]
p1.1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 1-50")
p1.1
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[50:100]
p1.2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 50-100")
p1.2
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[100:150]
p1.3 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 100-150")
p1.3
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[150:200]
p1.4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 150-200")
p1.4
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[200:250]
p1.5 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 200-250")
p1.5
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[250:300]
p1.6 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 250-300")
p1.6
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- pd.genes[300:330]
p1.7 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change PD Genes 300-330")
p1.7
NA
NA
pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/HM_PD_genes_Log2FC_dendrogram.pdf")
p1
p1.1
p1.2
p1.3
p1.4
p1.5
p1.6
p1.7
p2
p3
p4
dev.off()
null device
1
Read synapse list
synapse.genes.list <- read_excel("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/List of genes for RNAseq or Proteomics/SYNAPSE_REDUCED_GENE_LIST.xlsx", col_names = FALSE)
New names:
• `` -> `...1`
head(synapse.genes.list)
colnames(synapse.genes.list) <- c("Symbol")
head(synapse.genes.list)
synapse.genes <- synapse.genes.list$Symbol
gene_list <- synapse.genes
#gene_list <- lysome$`Gene name`
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes")
print(p1)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO")
p2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes")
print(p2)
contrast_list <- c("IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p3 <-plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes")
print(p3)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
p4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes")
print(p4)
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[1:100]
p1.1 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 1-100")
p1.1
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[100:200]
p1.2 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 100-200")
p1.2
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[200:300]
p1.3 <-plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 200-300")
p1.3
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[300:400]
p1.4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 300-400")
p1.4
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[400:500]
p1.5 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 400-500")
p1.5
contrast_list <- c("SNCA-A53T", "GBA-KO", "PINK1-KO", "PRKN-KO", "IGSF9B-KO","INPP5F-KO","SH3GL2-KO","IP6K2-KO")
gene_list <- synapse.genes[500:594]
p1.6 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = TRUE, column_width = 1, title = "Log Fold Change Synapse Genes 500-594")
p1.6
save the synaptic gene list
pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/HM_Synpatic_genes_Log2FC_dendrogram.pdf")
p1
p1.1
p1.2
p1.3
p1.4
p1.5
p1.6
p2
p3
p4
dev.off()
null device
1
GWAS PD
gwas.genes <- read.csv("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/List of genes for RNAseq or Proteomics/ALL_PD_GWAS_GENELIST.csv")
head(gwas.genes)
All gwas genes
print(gene_list)
[1] "ASXL3" "BAG3" "BIN3" "BRIP1" "BST1" "C5orf24" "CAB39L" "CAMK2D" "CASC16" "CD19"
[11] "CHRNB1" "CLCN3" "CRHR1" "CRLS1" "CTSB" "DDX46" "DGKQ" "DLG2" "DLST" "DNAH17"
[21] "DYRK1A" "EHMT2" "ELOVL7" "FAM171A2" "FAM47E" "FAM47E-STBD1" "FAM49B" "FBRSL1" "FCGR2A" "FGF11"
[31] "GAK" "GALC" "GBA" "GBAP1" "GBF1" "GCH1" "GPNMB" "GPR65" "GRN" "HIP1R"
[41] "IGSF9B" "INPP5F" "IP6K2" "ITGA8" "ITPKB" "KANSL1" "KCNIP3" "KCNS3" "KPNA1" "KRTCAP2"
[51] "LCORL" "LINC00693" "LRRK2" "MAP4K4" "MAPT-AS1" "MBNL2" "MCCC1" "MED12L" "MIPOL1" "MIR4308"
[61] "MRVI1-AS1" "MUC19" "NOD2" "NSF" "NUCKS1" "P2RY12" "PAM" "PGS1" "PMVK" "RAB7L1"
[71] "RABEP2" "RIT2" "RNF141" "RPS6KL1" "SCARB2" "SEMA4A" "SETD1A" "SH3GL2" "SIPA1L2" "SLC25A44"
[81] "SLC44A4" "SNCA" "SPPL2B" "SPPL2C" "SPTSSB" "STK39" "SYT17" "TBC1D5" "TMEM163" "TMEM175"
[91] "TRIM40" "UBAP2" "UBTF" "VAMP4" "WNT3" "ZBTB4"
p1.4 <- plot_logfold_change_heatmap_dendrogram(df_list_numeric, gene_list, contrast_list, remove_na_genes = FALSE, column_width = 1, title = "Log Fold Change GWAS Genes with distance < 10000")
p1.4
pdf("/Users/rhalenathomas/Downloads/Move_Data/DarkGenomeResult/omics/HM_GWAS_genes_Log2FC_dendrogram.pdf")
p1
p1.1
p1.2
p1.3
p1.4
p2
p3
p4
dev.off()